Deep learning project :¶

This project was carried out in 2nd year of engineering (BAC+4) at ESILV for the Deep Learning exam.

The objective of this project is to determine whether the patient presents a cardiac pathology according to his physical characteristics.

the dataset is described as follows: image.png

The ariables to be predicted are : Absence (1) or presence (2) of heart disease

Libraries :¶

In [1]:
from __future__ import absolute_import , division , print_function , unicode_literals

3 # TensorFlow and tf. keras
import tensorflow as tf
from tensorflow import keras
from random import seed
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from sklearn.utils import shuffle
from tensorflow.keras.optimizers import Adagrad, Adam, RMSprop, SGD
from sklearn.model_selection import train_test_split
from keras.callbacks import ModelCheckpoint
from keras.callbacks import EarlyStopping
from keras.models import load_model
from keras.layers import BatchNormalization
from tensorflow.keras.utils import to_categorical
from sklearn.utils import shuffle
from tensorflow.keras import regularizers
from sklearn.metrics import accuracy_score
# create dataset

Importation of the dataset :¶

There is 40 datasets (with 216 rows) of heart disease information. The exam is split in two parts :

  • Modelization with a large dataset : Here we use 35 datasets for the training and 5 datasets for the validation.
  • Modelization with a small dataset : Here we use one dataset (the number 33) for the training and one dataset of 54 rows for the validation.

Large dataset:¶

Training sets :¶

In [ ]:
df=pd.DataFrame()
for i in range(1,36):
  globals()[f"df_{i}"] = pd.read_csv(f"heart_train_{i}.txt", sep=" ",header=None)
  df = pd.concat([df, globals()[f"df_{i}"]])
df
Out[ ]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13
0 50.0 0.0 2.0 120.0 244.0 0.0 0.0 162.0 0.0 1.1 1.0 0.0 3.0 1
1 56.0 1.0 4.0 130.0 283.0 1.0 2.0 103.0 1.0 1.6 3.0 0.0 7.0 2
2 44.0 1.0 3.0 120.0 226.0 0.0 0.0 169.0 0.0 0.0 1.0 0.0 3.0 1
3 55.0 1.0 4.0 160.0 289.0 0.0 2.0 145.0 1.0 0.8 2.0 1.0 7.0 2
4 54.0 1.0 2.0 108.0 309.0 0.0 0.0 156.0 0.0 0.0 1.0 0.0 7.0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
211 44.0 0.0 3.0 108.0 141.0 0.0 0.0 175.0 0.0 0.6 2.0 0.0 3.0 1
212 50.0 1.0 3.0 129.0 196.0 0.0 0.0 163.0 0.0 0.0 1.0 0.0 3.0 1
213 63.0 0.0 4.0 150.0 407.0 0.0 2.0 154.0 0.0 4.0 2.0 3.0 7.0 2
214 50.0 1.0 4.0 150.0 243.0 0.0 2.0 128.0 0.0 2.6 2.0 0.0 7.0 2
215 45.0 0.0 4.0 138.0 236.0 0.0 2.0 152.0 1.0 0.2 2.0 0.0 3.0 1

7560 rows × 14 columns

In [ ]:
df=df.rename(columns={0:"age",1:"sex",2:"chest_pain",3:"blood_pressure",4:"cholestoral",5:"blood_sugar",6:"electrocardiographic",7:"max_heart_rate",8:"exercise",9:"oldpeak",10:"peak_exer",11:"nb_vessel",12:"defect",13:"disease"})
df
Out[ ]:
age sex chest_pain blood_pressure cholestoral blood_sugar electrocardiographic max_heart_rate exercise oldpeak peak_exer nb_vessel defect disease
0 50.0 0.0 2.0 120.0 244.0 0.0 0.0 162.0 0.0 1.1 1.0 0.0 3.0 1
1 56.0 1.0 4.0 130.0 283.0 1.0 2.0 103.0 1.0 1.6 3.0 0.0 7.0 2
2 44.0 1.0 3.0 120.0 226.0 0.0 0.0 169.0 0.0 0.0 1.0 0.0 3.0 1
3 55.0 1.0 4.0 160.0 289.0 0.0 2.0 145.0 1.0 0.8 2.0 1.0 7.0 2
4 54.0 1.0 2.0 108.0 309.0 0.0 0.0 156.0 0.0 0.0 1.0 0.0 7.0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
211 44.0 0.0 3.0 108.0 141.0 0.0 0.0 175.0 0.0 0.6 2.0 0.0 3.0 1
212 50.0 1.0 3.0 129.0 196.0 0.0 0.0 163.0 0.0 0.0 1.0 0.0 3.0 1
213 63.0 0.0 4.0 150.0 407.0 0.0 2.0 154.0 0.0 4.0 2.0 3.0 7.0 2
214 50.0 1.0 4.0 150.0 243.0 0.0 2.0 128.0 0.0 2.6 2.0 0.0 7.0 2
215 45.0 0.0 4.0 138.0 236.0 0.0 2.0 152.0 1.0 0.2 2.0 0.0 3.0 1

7560 rows × 14 columns

In [ ]:
df.columns
Out[ ]:
Index(['age', 'sex', 'chest_pain', 'blood_pressure', 'cholestoral',
       'blood_sugar', 'electrocardiographic', 'max_heart_rate', 'exercise',
       'oldpeak', 'peak_exer', 'nb_vessel', 'defect', 'disease'],
      dtype='object')
In [ ]:
df.to_csv("heart_train.txt",sep=" ", index=False)
#we save the dataset
In [ ]:
df= pd.read_csv(f"heart_train.txt", sep=" ")
df
Out[ ]:
Unnamed: 0 age sex chest_pain blood_pressure cholestoral blood_sugar electrocardiographic max_heart_rate exercise oldpeak peak_exer nb_vessel defect disease
0 0 50.0 0.0 2.0 120.0 244.0 0.0 0.0 162.0 0.0 1.1 1.0 0.0 3.0 1
1 1 56.0 1.0 4.0 130.0 283.0 1.0 2.0 103.0 1.0 1.6 3.0 0.0 7.0 2
2 2 44.0 1.0 3.0 120.0 226.0 0.0 0.0 169.0 0.0 0.0 1.0 0.0 3.0 1
3 3 55.0 1.0 4.0 160.0 289.0 0.0 2.0 145.0 1.0 0.8 2.0 1.0 7.0 2
4 4 54.0 1.0 2.0 108.0 309.0 0.0 0.0 156.0 0.0 0.0 1.0 0.0 7.0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7555 211 44.0 0.0 3.0 108.0 141.0 0.0 0.0 175.0 0.0 0.6 2.0 0.0 3.0 1
7556 212 50.0 1.0 3.0 129.0 196.0 0.0 0.0 163.0 0.0 0.0 1.0 0.0 3.0 1
7557 213 63.0 0.0 4.0 150.0 407.0 0.0 2.0 154.0 0.0 4.0 2.0 3.0 7.0 2
7558 214 50.0 1.0 4.0 150.0 243.0 0.0 2.0 128.0 0.0 2.6 2.0 0.0 7.0 2
7559 215 45.0 0.0 4.0 138.0 236.0 0.0 2.0 152.0 1.0 0.2 2.0 0.0 3.0 1

7560 rows × 15 columns

Validation set :¶

In [ ]:
validation=pd.DataFrame()
for i in range(36,41):
  globals()[f"df_{i}"] = pd.read_csv(f"heart_train_{i}.txt", sep=" ",header=None)
  validation = pd.concat([validation, globals()[f"df_{i}"]])
validation
Out[ ]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13
0 47.0 1.0 4.0 112.0 204.0 0.0 0.0 143.0 0.0 0.1 1.0 0.0 3.0 1
1 62.0 0.0 4.0 138.0 294.0 1.0 0.0 106.0 0.0 1.9 2.0 3.0 3.0 2
2 55.0 1.0 4.0 160.0 289.0 0.0 2.0 145.0 1.0 0.8 2.0 1.0 7.0 2
3 71.0 0.0 4.0 112.0 149.0 0.0 0.0 125.0 0.0 1.6 2.0 0.0 3.0 1
4 67.0 0.0 3.0 115.0 564.0 0.0 2.0 160.0 0.0 1.6 2.0 0.0 7.0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
211 56.0 1.0 1.0 120.0 193.0 0.0 2.0 162.0 0.0 1.9 2.0 0.0 7.0 1
212 49.0 0.0 2.0 134.0 271.0 0.0 0.0 162.0 0.0 0.0 2.0 0.0 3.0 1
213 54.0 0.0 3.0 110.0 214.0 0.0 0.0 158.0 0.0 1.6 2.0 0.0 3.0 1
214 41.0 0.0 2.0 130.0 204.0 0.0 2.0 172.0 0.0 1.4 1.0 0.0 3.0 1
215 66.0 0.0 4.0 178.0 228.0 1.0 0.0 165.0 1.0 1.0 2.0 2.0 7.0 2

1080 rows × 14 columns

In [ ]:
validation=validation.rename(columns={0:"age",1:"sex",2:"chest_pain",3:"blood_pressure",4:"cholestoral",5:"blood_sugar",6:"electrocardiographic",7:"max_heart_rate",8:"exercise",9:"oldpeak",10:"peak_exer",11:"nb_vessel",12:"defect",13:"disease"})
validation
Out[ ]:
age sex chest_pain blood_pressure cholestoral blood_sugar electrocardiographic max_heart_rate exercise oldpeak peak_exer nb_vessel defect disease
0 47.0 1.0 4.0 112.0 204.0 0.0 0.0 143.0 0.0 0.1 1.0 0.0 3.0 1
1 62.0 0.0 4.0 138.0 294.0 1.0 0.0 106.0 0.0 1.9 2.0 3.0 3.0 2
2 55.0 1.0 4.0 160.0 289.0 0.0 2.0 145.0 1.0 0.8 2.0 1.0 7.0 2
3 71.0 0.0 4.0 112.0 149.0 0.0 0.0 125.0 0.0 1.6 2.0 0.0 3.0 1
4 67.0 0.0 3.0 115.0 564.0 0.0 2.0 160.0 0.0 1.6 2.0 0.0 7.0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
211 56.0 1.0 1.0 120.0 193.0 0.0 2.0 162.0 0.0 1.9 2.0 0.0 7.0 1
212 49.0 0.0 2.0 134.0 271.0 0.0 0.0 162.0 0.0 0.0 2.0 0.0 3.0 1
213 54.0 0.0 3.0 110.0 214.0 0.0 0.0 158.0 0.0 1.6 2.0 0.0 3.0 1
214 41.0 0.0 2.0 130.0 204.0 0.0 2.0 172.0 0.0 1.4 1.0 0.0 3.0 1
215 66.0 0.0 4.0 178.0 228.0 1.0 0.0 165.0 1.0 1.0 2.0 2.0 7.0 2

1080 rows × 14 columns

In [ ]:
validation.to_csv("heart_validation.txt",sep=" ", index=False)
#we save the dataset

Small dataset¶

Training dataset :¶

In [5]:
small_df = pd.read_csv(f"heart_train_33.txt", sep=" ",header=None)
small_df=small_df.rename(columns={0:"age",1:"sex",2:"chest_pain",3:"blood_pressure",4:"cholestoral",5:"blood_sugar",6:"electrocardiographic",7:"max_heart_rate",8:"exercise",9:"oldpeak",10:"peak_exer",11:"nb_vessel",12:"defect",13:"disease"})
small_df
Out[5]:
age sex chest_pain blood_pressure cholestoral blood_sugar electrocardiographic max_heart_rate exercise oldpeak peak_exer nb_vessel defect disease
0 67.0 0.0 4.0 106.0 223.0 0.0 0.0 142.0 0.0 0.3 1.0 2.0 3.0 1
1 52.0 1.0 2.0 134.0 201.0 0.0 0.0 158.0 0.0 0.8 1.0 1.0 3.0 1
2 50.0 0.0 2.0 120.0 244.0 0.0 0.0 162.0 0.0 1.1 1.0 0.0 3.0 1
3 57.0 1.0 4.0 165.0 289.0 1.0 2.0 124.0 0.0 1.0 2.0 3.0 7.0 2
4 48.0 1.0 3.0 124.0 255.0 1.0 0.0 175.0 0.0 0.0 1.0 2.0 3.0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
211 42.0 1.0 3.0 130.0 180.0 0.0 0.0 150.0 0.0 0.0 1.0 0.0 3.0 1
212 57.0 0.0 4.0 120.0 354.0 0.0 0.0 163.0 1.0 0.6 1.0 0.0 3.0 1
213 65.0 0.0 4.0 150.0 225.0 0.0 2.0 114.0 0.0 1.0 2.0 3.0 7.0 2
214 38.0 1.0 1.0 120.0 231.0 0.0 0.0 182.0 1.0 3.8 2.0 0.0 7.0 2
215 47.0 1.0 4.0 112.0 204.0 0.0 0.0 143.0 0.0 0.1 1.0 0.0 3.0 1

216 rows × 14 columns

Validation :¶

In [4]:
small_validation = pd.read_csv(f"heart_test.txt", sep=" ",header=None)
small_validation
Out[4]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13
0 63.0 0.0 3.0 135.0 252.0 0.0 2.0 172.0 0.0 0.0 1.0 0.0 3.0 1
1 51.0 1.0 3.0 94.0 227.0 0.0 0.0 154.0 1.0 0.0 1.0 1.0 7.0 1
2 54.0 1.0 3.0 120.0 258.0 0.0 2.0 147.0 0.0 0.4 2.0 0.0 7.0 1
3 44.0 1.0 2.0 120.0 220.0 0.0 0.0 170.0 0.0 0.0 1.0 0.0 3.0 1
4 54.0 1.0 4.0 110.0 239.0 0.0 0.0 126.0 1.0 2.8 2.0 1.0 7.0 2
5 65.0 1.0 4.0 135.0 254.0 0.0 2.0 127.0 0.0 2.8 2.0 1.0 7.0 2
6 57.0 1.0 3.0 150.0 168.0 0.0 0.0 174.0 0.0 1.6 1.0 0.0 3.0 1
7 63.0 1.0 4.0 130.0 330.0 1.0 2.0 132.0 1.0 1.8 1.0 3.0 7.0 2
8 35.0 0.0 4.0 138.0 183.0 0.0 0.0 182.0 0.0 1.4 1.0 0.0 3.0 1
9 41.0 1.0 2.0 135.0 203.0 0.0 0.0 132.0 0.0 0.0 2.0 0.0 6.0 1
10 62.0 0.0 3.0 130.0 263.0 0.0 0.0 97.0 0.0 1.2 2.0 1.0 7.0 2
11 43.0 0.0 4.0 132.0 341.0 1.0 2.0 136.0 1.0 3.0 2.0 0.0 7.0 2
12 58.0 0.0 1.0 150.0 283.0 1.0 2.0 162.0 0.0 1.0 1.0 0.0 3.0 1
13 52.0 1.0 1.0 118.0 186.0 0.0 2.0 190.0 0.0 0.0 2.0 0.0 6.0 1
14 61.0 0.0 4.0 145.0 307.0 0.0 2.0 146.0 1.0 1.0 2.0 0.0 7.0 2
15 39.0 1.0 4.0 118.0 219.0 0.0 0.0 140.0 0.0 1.2 2.0 0.0 7.0 2
16 45.0 1.0 4.0 115.0 260.0 0.0 2.0 185.0 0.0 0.0 1.0 0.0 3.0 1
17 52.0 1.0 4.0 128.0 255.0 0.0 0.0 161.0 1.0 0.0 1.0 1.0 7.0 2
18 62.0 1.0 3.0 130.0 231.0 0.0 0.0 146.0 0.0 1.8 2.0 3.0 7.0 1
19 62.0 0.0 4.0 160.0 164.0 0.0 2.0 145.0 0.0 6.2 3.0 3.0 7.0 2
20 53.0 0.0 4.0 138.0 234.0 0.0 2.0 160.0 0.0 0.0 1.0 0.0 3.0 1
21 43.0 1.0 4.0 120.0 177.0 0.0 2.0 120.0 1.0 2.5 2.0 0.0 7.0 2
22 47.0 1.0 3.0 138.0 257.0 0.0 2.0 156.0 0.0 0.0 1.0 0.0 3.0 1
23 52.0 1.0 2.0 120.0 325.0 0.0 0.0 172.0 0.0 0.2 1.0 0.0 3.0 1
24 68.0 1.0 3.0 180.0 274.0 1.0 2.0 150.0 1.0 1.6 2.0 0.0 7.0 2
25 39.0 1.0 3.0 140.0 321.0 0.0 2.0 182.0 0.0 0.0 1.0 0.0 3.0 1
26 53.0 0.0 4.0 130.0 264.0 0.0 2.0 143.0 0.0 0.4 2.0 0.0 3.0 1
27 62.0 0.0 4.0 140.0 268.0 0.0 2.0 160.0 0.0 3.6 3.0 2.0 3.0 2
28 51.0 0.0 3.0 140.0 308.0 0.0 2.0 142.0 0.0 1.5 1.0 1.0 3.0 1
29 60.0 1.0 4.0 130.0 253.0 0.0 0.0 144.0 1.0 1.4 1.0 1.0 7.0 2
30 65.0 1.0 4.0 110.0 248.0 0.0 2.0 158.0 0.0 0.6 1.0 2.0 6.0 2
31 65.0 0.0 3.0 155.0 269.0 0.0 0.0 148.0 0.0 0.8 1.0 0.0 3.0 1
32 60.0 1.0 3.0 140.0 185.0 0.0 2.0 155.0 0.0 3.0 2.0 0.0 3.0 2
33 60.0 1.0 4.0 145.0 282.0 0.0 2.0 142.0 1.0 2.8 2.0 2.0 7.0 2
34 54.0 1.0 4.0 120.0 188.0 0.0 0.0 113.0 0.0 1.4 2.0 1.0 7.0 2
35 44.0 1.0 2.0 130.0 219.0 0.0 2.0 188.0 0.0 0.0 1.0 0.0 3.0 1
36 44.0 1.0 4.0 112.0 290.0 0.0 2.0 153.0 0.0 0.0 1.0 1.0 3.0 2
37 51.0 1.0 3.0 110.0 175.0 0.0 0.0 123.0 0.0 0.6 1.0 0.0 3.0 1
38 59.0 1.0 3.0 150.0 212.0 1.0 0.0 157.0 0.0 1.6 1.0 0.0 3.0 1
39 71.0 0.0 2.0 160.0 302.0 0.0 0.0 162.0 0.0 0.4 1.0 2.0 3.0 1
40 61.0 1.0 3.0 150.0 243.0 1.0 0.0 137.0 1.0 1.0 2.0 0.0 3.0 1
41 55.0 1.0 4.0 132.0 353.0 0.0 0.0 132.0 1.0 1.2 2.0 1.0 7.0 2
42 64.0 1.0 3.0 140.0 335.0 0.0 0.0 158.0 0.0 0.0 1.0 0.0 3.0 2
43 43.0 1.0 4.0 150.0 247.0 0.0 0.0 171.0 0.0 1.5 1.0 0.0 3.0 1
44 58.0 0.0 3.0 120.0 340.0 0.0 0.0 172.0 0.0 0.0 1.0 0.0 3.0 1
45 60.0 1.0 4.0 130.0 206.0 0.0 2.0 132.0 1.0 2.4 2.0 2.0 7.0 2
46 58.0 1.0 2.0 120.0 284.0 0.0 2.0 160.0 0.0 1.8 2.0 0.0 3.0 2
47 49.0 1.0 2.0 130.0 266.0 0.0 0.0 171.0 0.0 0.6 1.0 0.0 3.0 1
48 48.0 1.0 2.0 110.0 229.0 0.0 0.0 168.0 0.0 1.0 3.0 0.0 7.0 2
49 52.0 1.0 3.0 172.0 199.0 1.0 0.0 162.0 0.0 0.5 1.0 0.0 7.0 1
50 44.0 1.0 2.0 120.0 263.0 0.0 0.0 173.0 0.0 0.0 1.0 0.0 7.0 1
51 56.0 0.0 2.0 140.0 294.0 0.0 2.0 153.0 0.0 1.3 2.0 0.0 3.0 1
52 57.0 1.0 4.0 140.0 192.0 0.0 0.0 148.0 0.0 0.4 2.0 0.0 6.0 1
53 67.0 1.0 4.0 160.0 286.0 0.0 2.0 108.0 1.0 1.5 2.0 3.0 3.0 2
In [15]:
small_validation=small_validation.rename(columns={0:"age",1:"sex",2:"chest_pain",3:"blood_pressure",4:"cholestoral",5:"blood_sugar",6:"electrocardiographic",7:"max_heart_rate",8:"exercise",9:"oldpeak",10:"peak_exer",11:"nb_vessel",12:"defect",13:"disease"})

first exploration of the dataset:¶

Here we will do some data visualizations, statistical analysis, etc.

In [ ]:
df.describe()
Out[ ]:
age sex chest_pain blood_pressure cholestoral blood_sugar electrocardiographic max_heart_rate exercise oldpeak peak_exer nb_vessel defect disease
count 7560.000000 7560.000000 7560.000000 7560.000000 7560.000000 7560.000000 7560.000000 7560.000000 7560.000000 7560.000000 7560.000000 7560.000000 7560.000000 7560.000000
mean 54.449074 0.666667 3.171296 130.777778 248.967593 0.152778 1.027778 149.148148 0.347222 1.026389 1.597222 0.689815 4.657407 1.444444
std 9.249095 0.471436 0.968697 18.083740 52.246423 0.359797 0.995038 23.757785 0.476119 1.129028 0.616025 0.943406 1.935003 0.496937
min 29.000000 0.000000 1.000000 94.000000 126.000000 0.000000 0.000000 71.000000 0.000000 0.000000 1.000000 0.000000 3.000000 1.000000
25% 47.750000 0.000000 3.000000 120.000000 212.750000 0.000000 0.000000 131.750000 0.000000 0.000000 1.000000 0.000000 3.000000 1.000000
50% 55.000000 1.000000 3.000000 130.000000 243.000000 0.000000 2.000000 153.500000 0.000000 0.800000 2.000000 0.000000 3.000000 1.000000
75% 61.000000 1.000000 4.000000 140.000000 276.250000 0.000000 2.000000 166.000000 1.000000 1.800000 2.000000 1.000000 7.000000 2.000000
max 77.000000 1.000000 4.000000 200.000000 564.000000 1.000000 2.000000 202.000000 1.000000 5.600000 3.000000 3.000000 7.000000 2.000000
In [ ]:
sns.heatmap(df.corr(), cmap = 'coolwarm')
Out[ ]:
<Axes: >

The correlation table highlights that heart problems are mainly linked to different metrics related to the heart (which is logical) but we can add that sex seems to play an important role in the diagnosis of heart disease. This can either highlight that a gender is more affected by heart disease or that the other gender is less likely to be diagnosed with heart disease. Futher analysis cannot be carried out because the sex data is binary and the code associated with male or female is not stated.

In [ ]:
sns.pairplot(df,hue='disease')
Out[ ]:
<seaborn.axisgrid.PairGrid at 0x7b65c00ff160>

In blue the persons without a disease and in orange the persons with a disease.

Data cleaning for deep learning modelisation:¶

Large dataset :¶

We pass the variable in binary in order to make a binary classification

In [ ]:
df['disease'] = df['disease'].replace({1:0,2:1})
X = df.drop(columns="disease")
y=df["disease"]
y=y.values
y=to_categorical(y)
y.shape
Out[ ]:
(7560, 2)
In [ ]:
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)
In [ ]:
X_train[:5]
Out[ ]:
age sex chest_pain blood_pressure cholestoral blood_sugar electrocardiographic max_heart_rate exercise oldpeak peak_exer nb_vessel defect
14 59.0 1.0 2.0 140.0 221.0 0.0 0.0 164.0 1.0 0.0 1.0 0.0 3.0
137 65.0 1.0 4.0 120.0 177.0 0.0 0.0 140.0 0.0 0.4 1.0 0.0 7.0
164 69.0 1.0 3.0 140.0 254.0 0.0 2.0 146.0 0.0 2.0 2.0 3.0 7.0
97 57.0 1.0 3.0 150.0 126.0 1.0 0.0 173.0 0.0 0.2 1.0 1.0 7.0
84 63.0 0.0 4.0 108.0 269.0 0.0 0.0 169.0 1.0 1.8 2.0 2.0 3.0
In [ ]:
Y_train[:5]
Out[ ]:
array([[1., 0.],
       [1., 0.],
       [0., 1.],
       [1., 0.],
       [0., 1.]], dtype=float32)
In [ ]:
print("X training set size:", len(X_train))
print("Y training set size:",len(Y_train))
print("X testing set size:",len(X_test))
print("Y testing set size:",len(Y_test))
X training set size: 5292
Y training set size: 5292
X testing set size: 2268
Y testing set size: 2268

We have numerical columns, each of them have different units. In order to reduce the impact of the units we normalize the data.

In [ ]:
mean = X_train.mean(axis=0)
std = X_train.std(axis=0)
X_train -= mean
X_train /= std
X_test -= mean
X_test /= std

Small dataset :¶

In [ ]:
small_df['disease'] = small_df['disease'].replace({1:0,2:1})
X = small_df.drop(columns="disease")
y=small_df["disease"]
y=y.values
y=to_categorical(y)
print("y shape : ",y.shape)
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)
#the dataset is really small so we reduce the test set to 20% of the dataset
print("X training set size:", len(X_train))
print("Y training set size:",len(Y_train))
print("X testing set size:",len(X_test))
print("Y testing set size:",len(Y_test))
mean = X_train.mean(axis=0)
std = X_train.std(axis=0)
X_train -= mean
X_train /= std
X_test -= mean
X_test /= std
y shape :  (216, 2)
X training set size: 172
Y training set size: 172
X testing set size: 44
Y testing set size: 44

Modelizations and tests :¶

This is a deep learning assignment so only the deep learning models will be displayed. It was a 3-hour exam so not all the deep learning modeling methods were done.

Useful functions :¶

In [ ]:
def smooth_curve(points, factor=0.9):
  """function to plot the learning curves"""
  smoothed_points = []
  for point in points:
    if smoothed_points:
      previous = smoothed_points[-1]
      smoothed_points.append(previous * factor + point * (1 - factor))
    else:
      smoothed_points.append(point)
  return smoothed_points
In [ ]:
def plot_history(history):
  """function to plot the learning curves of the model"""
  # plot learning curves
  plt.plot(history.history['accuracy'], label='train')
  plt.plot(history.history['val_accuracy'], label='test')
  plt.title('lrate='+str(lrate), pad=-50)
  plt.legend()
  plt.show()
  ####################################
  plt.plot(smooth_curve(history.history['accuracy']), label='train')
  plt.plot(smooth_curve(history.history['val_accuracy']), label='test')
  plt.title('lrate='+str(lrate), pad=-50)
  plt.legend()
  plt.show()
  #####################
  plt.plot(smooth_curve(history.history['loss']), label='train')
  plt.plot(smooth_curve(history.history['val_loss']), label='test')
  plt.title('lrate='+str(lrate), pad=-50)
  plt.legend()
  plt.show()
In [ ]:
def creation_model0(X_train,Y_train,X_test,Y_test,earlystop,opti,lrate,l2reg):
  """function to select the optimization method of a model"""
  model = keras.Sequential([
    keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform',kernel_regularizer=keras.regularizers.l2(l2reg)),
    #13 neurons because it's the number of columns/inputs
    keras.layers.Dense(2, activation='sigmoid')
    #2 neurons because we want a classification with two labels
])
  es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=earlystop)
  mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
  if opti=="adam":
    opt=Adam(learning_rate= lrate)
  elif opti=="adagrad":
    opt=Adagrad(learning_rate= lrate)
  elif opti=="SGD":
    opt=SGD(learning_rate=0.1, momentum=0.9)
  else :
    opt= RMSprop(learning_rate= lrate, momentum=0.9)
  model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
  history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0, callbacks=[es, mc])

  # plot learning curves
  plt.plot(history.history['accuracy'], label='train')
  plt.plot(history.history['val_accuracy'], label='test')
  plt.title('lrate='+str(lrate), pad=-50)
  plt.legend()
  plt.show()
  ####################################
  plt.plot(smooth_curve(history.history['accuracy']), label='train')
  plt.plot(smooth_curve(history.history['val_accuracy']), label='test')
  plt.title('lrate='+str(lrate), pad=-50)
  plt.legend()
  plt.show()
  #####################
  plt.plot(smooth_curve(history.history['loss']), label='train')
  plt.plot(smooth_curve(history.history['val_loss']), label='test')
  plt.title('lrate='+str(lrate), pad=-50)
  plt.legend()
  plt.show()

Modelizations with the large dataset :¶

First test:

In [ ]:
mod0 = keras.Sequential([
    keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform'),
    #13 neurons because it's the number of columns/inputs
    keras.layers.Dense(2, activation='sigmoid')
    #2 neurons because we want a classification with two labels
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=20)
mc = ModelCheckpoint('best_model.h0', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
lrate = 0.005
mod0.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate= lrate), metrics=['accuracy'])
history = mod0.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0,callbacks=[es, mc])
saved_model = load_model('best_model.h0')
Epoch 1: val_accuracy improved from -inf to 0.63183, saving model to best_model.h0

Epoch 2: val_accuracy improved from 0.63183 to 0.70503, saving model to best_model.h0

Epoch 3: val_accuracy improved from 0.70503 to 0.75970, saving model to best_model.h0

Epoch 4: val_accuracy improved from 0.75970 to 0.79982, saving model to best_model.h0

Epoch 5: val_accuracy improved from 0.79982 to 0.80511, saving model to best_model.h0

Epoch 6: val_accuracy did not improve from 0.80511

Epoch 7: val_accuracy improved from 0.80511 to 0.82275, saving model to best_model.h0

Epoch 8: val_accuracy did not improve from 0.82275

Epoch 9: val_accuracy improved from 0.82275 to 0.82363, saving model to best_model.h0

Epoch 10: val_accuracy improved from 0.82363 to 0.84259, saving model to best_model.h0

Epoch 11: val_accuracy did not improve from 0.84259

Epoch 12: val_accuracy improved from 0.84259 to 0.84303, saving model to best_model.h0

Epoch 13: val_accuracy improved from 0.84303 to 0.84965, saving model to best_model.h0

Epoch 14: val_accuracy did not improve from 0.84965

Epoch 15: val_accuracy did not improve from 0.84965

Epoch 16: val_accuracy did not improve from 0.84965

Epoch 17: val_accuracy did not improve from 0.84965

Epoch 18: val_accuracy did not improve from 0.84965

Epoch 19: val_accuracy did not improve from 0.84965

Epoch 20: val_accuracy did not improve from 0.84965

Epoch 21: val_accuracy did not improve from 0.84965

Epoch 22: val_accuracy did not improve from 0.84965

Epoch 23: val_accuracy did not improve from 0.84965

Epoch 24: val_accuracy did not improve from 0.84965

Epoch 25: val_accuracy did not improve from 0.84965

Epoch 26: val_accuracy did not improve from 0.84965

Epoch 27: val_accuracy did not improve from 0.84965

Epoch 28: val_accuracy did not improve from 0.84965

Epoch 29: val_accuracy did not improve from 0.84965

Epoch 30: val_accuracy did not improve from 0.84965

Epoch 31: val_accuracy did not improve from 0.84965

Epoch 32: val_accuracy did not improve from 0.84965

Epoch 33: val_accuracy did not improve from 0.84965
In [ ]:
mod0.summary()
Model: "sequential_2"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_4 (Dense)             (None, 13)                182       
                                                                 
 dense_5 (Dense)             (None, 2)                 28        
                                                                 
=================================================================
Total params: 210 (840.00 Byte)
Trainable params: 210 (840.00 Byte)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
In [ ]:
plot_history(history)

As you can see the model doesn't overfit (2nd and 3rd graph) but the performance can probably be improved. We will try to add an hidden layer.

In [ ]:
mod1 = keras.Sequential([
    keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
    keras.layers.Dense(7, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
    #mean(13+2) = 7.5 ~ 7 or 8 for the number of neurons in the hidden layer
    keras.layers.Dense(2, activation='sigmoid')
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=35)
mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
lrate = 0.005
mod1.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate= lrate), metrics=['accuracy'])
history1 = mod1.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0,callbacks=[es, mc])
Epoch 1: val_accuracy improved from -inf to 0.57231, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 2: val_accuracy improved from 0.57231 to 0.71252, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.71252 to 0.75132, saving model to best_model.h5

Epoch 4: val_accuracy improved from 0.75132 to 0.78483, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.78483 to 0.80511, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.80511 to 0.82496, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.82496 to 0.83113, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.83113

Epoch 9: val_accuracy did not improve from 0.83113

Epoch 10: val_accuracy improved from 0.83113 to 0.84171, saving model to best_model.h5

Epoch 11: val_accuracy improved from 0.84171 to 0.85009, saving model to best_model.h5

Epoch 12: val_accuracy improved from 0.85009 to 0.85582, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.85582

Epoch 14: val_accuracy improved from 0.85582 to 0.85979, saving model to best_model.h5

Epoch 15: val_accuracy did not improve from 0.85979

Epoch 16: val_accuracy improved from 0.85979 to 0.86464, saving model to best_model.h5

Epoch 17: val_accuracy did not improve from 0.86464

Epoch 18: val_accuracy improved from 0.86464 to 0.87522, saving model to best_model.h5

Epoch 19: val_accuracy improved from 0.87522 to 0.88492, saving model to best_model.h5

Epoch 20: val_accuracy improved from 0.88492 to 0.88889, saving model to best_model.h5

Epoch 21: val_accuracy improved from 0.88889 to 0.89242, saving model to best_model.h5

Epoch 22: val_accuracy did not improve from 0.89242

Epoch 23: val_accuracy improved from 0.89242 to 0.89771, saving model to best_model.h5

Epoch 24: val_accuracy did not improve from 0.89771

Epoch 25: val_accuracy did not improve from 0.89771

Epoch 26: val_accuracy improved from 0.89771 to 0.90123, saving model to best_model.h5

Epoch 27: val_accuracy improved from 0.90123 to 0.90653, saving model to best_model.h5

Epoch 28: val_accuracy improved from 0.90653 to 0.91446, saving model to best_model.h5

Epoch 29: val_accuracy improved from 0.91446 to 0.92240, saving model to best_model.h5

Epoch 30: val_accuracy improved from 0.92240 to 0.92593, saving model to best_model.h5

Epoch 31: val_accuracy improved from 0.92593 to 0.92945, saving model to best_model.h5

Epoch 32: val_accuracy did not improve from 0.92945

Epoch 33: val_accuracy did not improve from 0.92945

Epoch 34: val_accuracy did not improve from 0.92945

Epoch 35: val_accuracy improved from 0.92945 to 0.93430, saving model to best_model.h5

Epoch 36: val_accuracy did not improve from 0.93430

Epoch 37: val_accuracy improved from 0.93430 to 0.94444, saving model to best_model.h5

Epoch 38: val_accuracy did not improve from 0.94444

Epoch 39: val_accuracy did not improve from 0.94444

Epoch 40: val_accuracy did not improve from 0.94444

Epoch 41: val_accuracy improved from 0.94444 to 0.95018, saving model to best_model.h5

Epoch 42: val_accuracy improved from 0.95018 to 0.95194, saving model to best_model.h5

Epoch 43: val_accuracy improved from 0.95194 to 0.95767, saving model to best_model.h5

Epoch 44: val_accuracy did not improve from 0.95767

Epoch 45: val_accuracy did not improve from 0.95767

Epoch 46: val_accuracy did not improve from 0.95767

Epoch 47: val_accuracy did not improve from 0.95767

Epoch 48: val_accuracy improved from 0.95767 to 0.96296, saving model to best_model.h5

Epoch 49: val_accuracy improved from 0.96296 to 0.96384, saving model to best_model.h5

Epoch 50: val_accuracy did not improve from 0.96384

Epoch 51: val_accuracy improved from 0.96384 to 0.97840, saving model to best_model.h5

Epoch 52: val_accuracy did not improve from 0.97840

Epoch 53: val_accuracy did not improve from 0.97840

Epoch 54: val_accuracy did not improve from 0.97840

Epoch 55: val_accuracy did not improve from 0.97840

Epoch 56: val_accuracy improved from 0.97840 to 0.98457, saving model to best_model.h5

Epoch 57: val_accuracy did not improve from 0.98457

Epoch 58: val_accuracy did not improve from 0.98457

Epoch 59: val_accuracy did not improve from 0.98457

Epoch 60: val_accuracy did not improve from 0.98457

Epoch 61: val_accuracy did not improve from 0.98457

Epoch 62: val_accuracy did not improve from 0.98457

Epoch 63: val_accuracy did not improve from 0.98457

Epoch 64: val_accuracy did not improve from 0.98457

Epoch 65: val_accuracy improved from 0.98457 to 0.98942, saving model to best_model.h5

Epoch 66: val_accuracy did not improve from 0.98942

Epoch 67: val_accuracy did not improve from 0.98942

Epoch 68: val_accuracy did not improve from 0.98942

Epoch 69: val_accuracy did not improve from 0.98942

Epoch 70: val_accuracy did not improve from 0.98942

Epoch 71: val_accuracy did not improve from 0.98942

Epoch 72: val_accuracy improved from 0.98942 to 0.99471, saving model to best_model.h5

Epoch 73: val_accuracy did not improve from 0.99471

Epoch 74: val_accuracy did not improve from 0.99471

Epoch 75: val_accuracy did not improve from 0.99471

Epoch 76: val_accuracy did not improve from 0.99471

Epoch 77: val_accuracy did not improve from 0.99471

Epoch 78: val_accuracy did not improve from 0.99471

Epoch 79: val_accuracy did not improve from 0.99471

Epoch 80: val_accuracy did not improve from 0.99471

Epoch 81: val_accuracy did not improve from 0.99471

Epoch 82: val_accuracy did not improve from 0.99471

Epoch 83: val_accuracy did not improve from 0.99471

Epoch 84: val_accuracy did not improve from 0.99471

Epoch 85: val_accuracy did not improve from 0.99471

Epoch 86: val_accuracy did not improve from 0.99471

Epoch 87: val_accuracy did not improve from 0.99471

Epoch 88: val_accuracy did not improve from 0.99471

Epoch 89: val_accuracy did not improve from 0.99471

Epoch 90: val_accuracy did not improve from 0.99471

Epoch 91: val_accuracy did not improve from 0.99471

Epoch 92: val_accuracy did not improve from 0.99471

Epoch 93: val_accuracy did not improve from 0.99471

Epoch 94: val_accuracy did not improve from 0.99471

Epoch 95: val_accuracy did not improve from 0.99471

Epoch 96: val_accuracy did not improve from 0.99471

Epoch 97: val_accuracy did not improve from 0.99471

Epoch 98: val_accuracy did not improve from 0.99471

Epoch 99: val_accuracy did not improve from 0.99471

Epoch 100: val_accuracy did not improve from 0.99471

Epoch 101: val_accuracy did not improve from 0.99471

Epoch 102: val_accuracy did not improve from 0.99471

Epoch 103: val_accuracy did not improve from 0.99471

Epoch 104: val_accuracy did not improve from 0.99471

Epoch 105: val_accuracy did not improve from 0.99471

Epoch 106: val_accuracy did not improve from 0.99471

Epoch 107: val_accuracy did not improve from 0.99471
In [ ]:
mod1.summary()
Model: "sequential_9"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_24 (Dense)            (None, 13)                182       
                                                                 
 batch_normalization_12 (Ba  (None, 13)                52        
 tchNormalization)                                               
                                                                 
 dense_25 (Dense)            (None, 7)                 98        
                                                                 
 batch_normalization_13 (Ba  (None, 7)                 28        
 tchNormalization)                                               
                                                                 
 dense_26 (Dense)            (None, 2)                 16        
                                                                 
=================================================================
Total params: 376 (1.47 KB)
Trainable params: 336 (1.31 KB)
Non-trainable params: 40 (160.00 Byte)
_________________________________________________________________
In [ ]:
plot_history(history1)

By adding one hidden layer we have increase the performances. We will test it with the other number of neurons recommanded.

In [ ]:
mod2 = keras.Sequential([
    keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
    keras.layers.Dense(8, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
    #mean(13+2) = 7.5 ~ 7 or 8 for the number of neurons in the hidden layer
    keras.layers.Dense(2, activation='sigmoid')
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=20)
mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
lrate = 0.005
mod2.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate= lrate), metrics=['accuracy'])
history2 = mod2.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0,callbacks=[es, mc])
Epoch 1: val_accuracy improved from -inf to 0.73677, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 2: val_accuracy improved from 0.73677 to 0.73765, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.73765 to 0.76455, saving model to best_model.h5

Epoch 4: val_accuracy improved from 0.76455 to 0.79189, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.79189 to 0.80291, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.80291

Epoch 7: val_accuracy improved from 0.80291 to 0.80423, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.80423 to 0.82099, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.82099

Epoch 10: val_accuracy improved from 0.82099 to 0.82407, saving model to best_model.h5

Epoch 11: val_accuracy improved from 0.82407 to 0.82937, saving model to best_model.h5

Epoch 12: val_accuracy improved from 0.82937 to 0.83466, saving model to best_model.h5

Epoch 13: val_accuracy improved from 0.83466 to 0.83862, saving model to best_model.h5

Epoch 14: val_accuracy improved from 0.83862 to 0.84259, saving model to best_model.h5

Epoch 15: val_accuracy improved from 0.84259 to 0.84524, saving model to best_model.h5

Epoch 16: val_accuracy improved from 0.84524 to 0.86243, saving model to best_model.h5

Epoch 17: val_accuracy did not improve from 0.86243

Epoch 18: val_accuracy improved from 0.86243 to 0.86772, saving model to best_model.h5

Epoch 19: val_accuracy improved from 0.86772 to 0.87831, saving model to best_model.h5

Epoch 20: val_accuracy improved from 0.87831 to 0.88757, saving model to best_model.h5

Epoch 21: val_accuracy did not improve from 0.88757

Epoch 22: val_accuracy improved from 0.88757 to 0.89109, saving model to best_model.h5

Epoch 23: val_accuracy improved from 0.89109 to 0.89374, saving model to best_model.h5

Epoch 24: val_accuracy did not improve from 0.89374

Epoch 25: val_accuracy improved from 0.89374 to 0.90873, saving model to best_model.h5

Epoch 26: val_accuracy improved from 0.90873 to 0.92240, saving model to best_model.h5

Epoch 27: val_accuracy did not improve from 0.92240

Epoch 28: val_accuracy did not improve from 0.92240

Epoch 29: val_accuracy did not improve from 0.92240

Epoch 30: val_accuracy did not improve from 0.92240

Epoch 31: val_accuracy did not improve from 0.92240

Epoch 32: val_accuracy improved from 0.92240 to 0.92593, saving model to best_model.h5

Epoch 33: val_accuracy improved from 0.92593 to 0.92945, saving model to best_model.h5

Epoch 34: val_accuracy improved from 0.92945 to 0.93563, saving model to best_model.h5

Epoch 35: val_accuracy did not improve from 0.93563

Epoch 36: val_accuracy did not improve from 0.93563

Epoch 37: val_accuracy did not improve from 0.93563

Epoch 38: val_accuracy did not improve from 0.93563

Epoch 39: val_accuracy improved from 0.93563 to 0.94577, saving model to best_model.h5

Epoch 40: val_accuracy improved from 0.94577 to 0.94797, saving model to best_model.h5

Epoch 41: val_accuracy improved from 0.94797 to 0.95062, saving model to best_model.h5

Epoch 42: val_accuracy did not improve from 0.95062

Epoch 43: val_accuracy did not improve from 0.95062

Epoch 44: val_accuracy did not improve from 0.95062

Epoch 45: val_accuracy did not improve from 0.95062

Epoch 46: val_accuracy improved from 0.95062 to 0.95767, saving model to best_model.h5

Epoch 47: val_accuracy did not improve from 0.95767

Epoch 48: val_accuracy did not improve from 0.95767

Epoch 49: val_accuracy did not improve from 0.95767

Epoch 50: val_accuracy did not improve from 0.95767

Epoch 51: val_accuracy did not improve from 0.95767

Epoch 52: val_accuracy improved from 0.95767 to 0.96384, saving model to best_model.h5

Epoch 53: val_accuracy did not improve from 0.96384

Epoch 54: val_accuracy did not improve from 0.96384

Epoch 55: val_accuracy did not improve from 0.96384

Epoch 56: val_accuracy did not improve from 0.96384

Epoch 57: val_accuracy did not improve from 0.96384

Epoch 58: val_accuracy did not improve from 0.96384

Epoch 59: val_accuracy did not improve from 0.96384

Epoch 60: val_accuracy improved from 0.96384 to 0.96649, saving model to best_model.h5

Epoch 61: val_accuracy did not improve from 0.96649

Epoch 62: val_accuracy did not improve from 0.96649

Epoch 63: val_accuracy did not improve from 0.96649

Epoch 64: val_accuracy did not improve from 0.96649

Epoch 65: val_accuracy did not improve from 0.96649

Epoch 66: val_accuracy did not improve from 0.96649

Epoch 67: val_accuracy improved from 0.96649 to 0.97840, saving model to best_model.h5

Epoch 68: val_accuracy did not improve from 0.97840

Epoch 69: val_accuracy did not improve from 0.97840

Epoch 70: val_accuracy did not improve from 0.97840

Epoch 71: val_accuracy did not improve from 0.97840

Epoch 72: val_accuracy did not improve from 0.97840

Epoch 73: val_accuracy did not improve from 0.97840

Epoch 74: val_accuracy did not improve from 0.97840

Epoch 75: val_accuracy did not improve from 0.97840

Epoch 76: val_accuracy improved from 0.97840 to 0.98192, saving model to best_model.h5

Epoch 77: val_accuracy did not improve from 0.98192

Epoch 78: val_accuracy did not improve from 0.98192

Epoch 79: val_accuracy did not improve from 0.98192

Epoch 80: val_accuracy did not improve from 0.98192

Epoch 81: val_accuracy did not improve from 0.98192

Epoch 82: val_accuracy did not improve from 0.98192

Epoch 83: val_accuracy did not improve from 0.98192

Epoch 84: val_accuracy did not improve from 0.98192

Epoch 85: val_accuracy did not improve from 0.98192

Epoch 86: val_accuracy improved from 0.98192 to 0.98457, saving model to best_model.h5

Epoch 87: val_accuracy did not improve from 0.98457

Epoch 88: val_accuracy did not improve from 0.98457

Epoch 89: val_accuracy did not improve from 0.98457

Epoch 90: val_accuracy did not improve from 0.98457

Epoch 91: val_accuracy did not improve from 0.98457

Epoch 92: val_accuracy did not improve from 0.98457

Epoch 93: val_accuracy did not improve from 0.98457

Epoch 94: val_accuracy did not improve from 0.98457

Epoch 95: val_accuracy did not improve from 0.98457

Epoch 96: val_accuracy did not improve from 0.98457

Epoch 97: val_accuracy did not improve from 0.98457

Epoch 98: val_accuracy did not improve from 0.98457

Epoch 99: val_accuracy did not improve from 0.98457

Epoch 100: val_accuracy did not improve from 0.98457

Epoch 101: val_accuracy did not improve from 0.98457

Epoch 102: val_accuracy did not improve from 0.98457

Epoch 103: val_accuracy did not improve from 0.98457

Epoch 104: val_accuracy did not improve from 0.98457

Epoch 105: val_accuracy did not improve from 0.98457

Epoch 106: val_accuracy improved from 0.98457 to 0.98986, saving model to best_model.h5

Epoch 107: val_accuracy improved from 0.98986 to 0.99515, saving model to best_model.h5

Epoch 108: val_accuracy did not improve from 0.99515

Epoch 109: val_accuracy did not improve from 0.99515

Epoch 110: val_accuracy did not improve from 0.99515

Epoch 111: val_accuracy did not improve from 0.99515

Epoch 112: val_accuracy did not improve from 0.99515

Epoch 113: val_accuracy did not improve from 0.99515

Epoch 114: val_accuracy did not improve from 0.99515

Epoch 115: val_accuracy did not improve from 0.99515

Epoch 116: val_accuracy did not improve from 0.99515

Epoch 117: val_accuracy did not improve from 0.99515

Epoch 118: val_accuracy did not improve from 0.99515

Epoch 119: val_accuracy did not improve from 0.99515

Epoch 120: val_accuracy did not improve from 0.99515

Epoch 121: val_accuracy did not improve from 0.99515

Epoch 122: val_accuracy did not improve from 0.99515

Epoch 123: val_accuracy did not improve from 0.99515

Epoch 124: val_accuracy did not improve from 0.99515

Epoch 125: val_accuracy did not improve from 0.99515

Epoch 126: val_accuracy did not improve from 0.99515

Epoch 127: val_accuracy did not improve from 0.99515
In [ ]:
mod2.summary()
Model: "sequential_10"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_27 (Dense)            (None, 13)                182       
                                                                 
 batch_normalization_14 (Ba  (None, 13)                52        
 tchNormalization)                                               
                                                                 
 dense_28 (Dense)            (None, 8)                 112       
                                                                 
 batch_normalization_15 (Ba  (None, 8)                 32        
 tchNormalization)                                               
                                                                 
 dense_29 (Dense)            (None, 2)                 18        
                                                                 
=================================================================
Total params: 396 (1.55 KB)
Trainable params: 354 (1.38 KB)
Non-trainable params: 42 (168.00 Byte)
_________________________________________________________________
In [ ]:
plot_history(history2)

By adding an additional neuron we further increased the performance of our models without overfitting.

The last models is excellent, so I select it as the final model for the prediction of the validation set.

Validation and final prediction:¶

In [ ]:
X = validation.drop(columns="disease")
y=validation["disease"]

y.shape
Out[ ]:
(1080,)
In [ ]:
X-= mean
X /= std
In [ ]:
predictions = mod2.predict(X)
34/34 [==============================] - 0s 2ms/step
In [ ]:
predictions[:5]
Out[ ]:
array([[8.4876359e-01, 1.2433447e-01],
       [3.5142712e-02, 9.6764618e-01],
       [1.7565981e-04, 9.9975723e-01],
       [9.9389571e-01, 4.6091015e-03],
       [9.8444611e-01, 1.3892172e-02]], dtype=float32)
In [ ]:
result =[]
for i in range(len(predictions)):
  if predictions[i][0]>predictions[i][1]:
    result.append(1)#the real class
  else :
    result.append(2)#the real class
In [ ]:
score = accuracy_score(validation["disease"], result)
print("accuracy : "+str(score))
accuracy : 0.9953703703703703
In [ ]:
#writing results for exam evaluation
r= open("results_big_dataset.txt", "w+")
r.write(str(result))
r.close()

Modelizations with the small dataset :¶

In [ ]:
mod0 = keras.Sequential([
    keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform'),
    #13 neurons because it's the number of columns/inputs
    keras.layers.Dense(2, activation='sigmoid')
    #2 neurons because we want a classification with two labels
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=20)
mc = ModelCheckpoint('best_model.h0', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
lrate = 0.005
mod0.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate= lrate), metrics=['accuracy'])
history = mod0.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0,callbacks=[es, mc])
Epoch 1: val_accuracy improved from -inf to 0.43182, saving model to best_model.h0

Epoch 2: val_accuracy did not improve from 0.43182

Epoch 3: val_accuracy did not improve from 0.43182

Epoch 4: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h0

Epoch 5: val_accuracy did not improve from 0.45455

Epoch 6: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h0

Epoch 7: val_accuracy did not improve from 0.47727

Epoch 8: val_accuracy did not improve from 0.47727

Epoch 9: val_accuracy did not improve from 0.47727

Epoch 10: val_accuracy did not improve from 0.47727

Epoch 11: val_accuracy did not improve from 0.47727

Epoch 12: val_accuracy did not improve from 0.47727

Epoch 13: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h0

Epoch 14: val_accuracy did not improve from 0.50000

Epoch 15: val_accuracy did not improve from 0.50000

Epoch 16: val_accuracy did not improve from 0.50000

Epoch 17: val_accuracy did not improve from 0.50000

Epoch 18: val_accuracy did not improve from 0.50000

Epoch 19: val_accuracy did not improve from 0.50000

Epoch 20: val_accuracy did not improve from 0.50000

Epoch 21: val_accuracy did not improve from 0.50000

Epoch 22: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h0

Epoch 23: val_accuracy improved from 0.52273 to 0.54545, saving model to best_model.h0

Epoch 24: val_accuracy did not improve from 0.54545

Epoch 25: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h0

Epoch 26: val_accuracy did not improve from 0.56818

Epoch 27: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h0

Epoch 28: val_accuracy did not improve from 0.59091

Epoch 29: val_accuracy did not improve from 0.59091

Epoch 30: val_accuracy did not improve from 0.59091

Epoch 31: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h0

Epoch 32: val_accuracy did not improve from 0.61364

Epoch 33: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h0

Epoch 34: val_accuracy did not improve from 0.63636

Epoch 35: val_accuracy did not improve from 0.63636

Epoch 36: val_accuracy did not improve from 0.63636

Epoch 37: val_accuracy did not improve from 0.63636

Epoch 38: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h0

Epoch 39: val_accuracy did not improve from 0.65909

Epoch 40: val_accuracy did not improve from 0.65909

Epoch 41: val_accuracy did not improve from 0.65909

Epoch 42: val_accuracy did not improve from 0.65909

Epoch 43: val_accuracy did not improve from 0.65909

Epoch 44: val_accuracy did not improve from 0.65909

Epoch 45: val_accuracy did not improve from 0.65909

Epoch 46: val_accuracy did not improve from 0.65909

Epoch 47: val_accuracy did not improve from 0.65909

Epoch 48: val_accuracy did not improve from 0.65909

Epoch 49: val_accuracy did not improve from 0.65909

Epoch 50: val_accuracy did not improve from 0.65909

Epoch 51: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h0

Epoch 52: val_accuracy did not improve from 0.68182

Epoch 53: val_accuracy did not improve from 0.68182

Epoch 54: val_accuracy did not improve from 0.68182

Epoch 55: val_accuracy did not improve from 0.68182

Epoch 56: val_accuracy did not improve from 0.68182

Epoch 57: val_accuracy did not improve from 0.68182

Epoch 58: val_accuracy did not improve from 0.68182

Epoch 59: val_accuracy did not improve from 0.68182

Epoch 60: val_accuracy did not improve from 0.68182

Epoch 61: val_accuracy did not improve from 0.68182

Epoch 62: val_accuracy did not improve from 0.68182

Epoch 63: val_accuracy did not improve from 0.68182

Epoch 64: val_accuracy did not improve from 0.68182

Epoch 65: val_accuracy did not improve from 0.68182

Epoch 66: val_accuracy did not improve from 0.68182

Epoch 67: val_accuracy did not improve from 0.68182

Epoch 68: val_accuracy did not improve from 0.68182

Epoch 69: val_accuracy did not improve from 0.68182

Epoch 70: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h0

Epoch 71: val_accuracy did not improve from 0.70455

Epoch 72: val_accuracy did not improve from 0.70455

Epoch 73: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h0

Epoch 74: val_accuracy did not improve from 0.72727

Epoch 75: val_accuracy did not improve from 0.72727

Epoch 76: val_accuracy did not improve from 0.72727

Epoch 77: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h0

Epoch 78: val_accuracy did not improve from 0.77273

Epoch 79: val_accuracy did not improve from 0.77273

Epoch 80: val_accuracy did not improve from 0.77273

Epoch 81: val_accuracy did not improve from 0.77273

Epoch 82: val_accuracy did not improve from 0.77273

Epoch 83: val_accuracy did not improve from 0.77273

Epoch 84: val_accuracy did not improve from 0.77273

Epoch 85: val_accuracy did not improve from 0.77273

Epoch 86: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h0

Epoch 87: val_accuracy did not improve from 0.79545

Epoch 88: val_accuracy did not improve from 0.79545

Epoch 89: val_accuracy did not improve from 0.79545

Epoch 90: val_accuracy did not improve from 0.79545

Epoch 91: val_accuracy did not improve from 0.79545

Epoch 92: val_accuracy did not improve from 0.79545

Epoch 93: val_accuracy did not improve from 0.79545

Epoch 94: val_accuracy did not improve from 0.79545

Epoch 95: val_accuracy did not improve from 0.79545

Epoch 96: val_accuracy did not improve from 0.79545

Epoch 97: val_accuracy did not improve from 0.79545

Epoch 98: val_accuracy did not improve from 0.79545

Epoch 99: val_accuracy did not improve from 0.79545

Epoch 100: val_accuracy did not improve from 0.79545

Epoch 101: val_accuracy did not improve from 0.79545

Epoch 102: val_accuracy did not improve from 0.79545

Epoch 103: val_accuracy did not improve from 0.79545

Epoch 104: val_accuracy did not improve from 0.79545

Epoch 105: val_accuracy did not improve from 0.79545

Epoch 106: val_accuracy did not improve from 0.79545
In [ ]:
mod0.summary()
Model: "sequential_12"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_32 (Dense)            (None, 13)                182       
                                                                 
 dense_33 (Dense)            (None, 2)                 28        
                                                                 
=================================================================
Total params: 210 (840.00 Byte)
Trainable params: 210 (840.00 Byte)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
In [ ]:
plot_history(history)

Performance is good for a first try with so little data. It overfit more than with the large dataset, but it's normal due to the lack of data. They are less good than with the large dataset because there is less data because it is more difficult to correctly generalize the information when there is a lack of data.

We will try to add one hidden layer.

In [ ]:
mod1 = keras.Sequential([
    keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
    keras.layers.Dense(7, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
    #mean(13+2) = 7.5 ~ 7 or 8 for the number of neurons in the hidden layer
    keras.layers.Dense(2, activation='sigmoid')
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=20)
mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
lrate = 0.005
mod1.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate= lrate), metrics=['accuracy'])
history1 = mod1.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0,callbacks=[es, mc])
Epoch 1: val_accuracy improved from -inf to 0.47727, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.47727

Epoch 3: val_accuracy improved from 0.47727 to 0.52273, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.52273 to 0.54545, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.54545

Epoch 6: val_accuracy did not improve from 0.54545

Epoch 7: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.56818

Epoch 9: val_accuracy did not improve from 0.56818

Epoch 10: val_accuracy did not improve from 0.56818

Epoch 11: val_accuracy did not improve from 0.56818

Epoch 12: val_accuracy did not improve from 0.56818

Epoch 13: val_accuracy did not improve from 0.56818

Epoch 14: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 15: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.61364

Epoch 17: val_accuracy did not improve from 0.61364

Epoch 18: val_accuracy did not improve from 0.61364

Epoch 19: val_accuracy did not improve from 0.61364

Epoch 20: val_accuracy did not improve from 0.61364

Epoch 21: val_accuracy did not improve from 0.61364

Epoch 22: val_accuracy did not improve from 0.61364

Epoch 23: val_accuracy did not improve from 0.61364

Epoch 24: val_accuracy did not improve from 0.61364

Epoch 25: val_accuracy did not improve from 0.61364

Epoch 26: val_accuracy did not improve from 0.61364

Epoch 27: val_accuracy did not improve from 0.61364

Epoch 28: val_accuracy did not improve from 0.61364

Epoch 29: val_accuracy did not improve from 0.61364

Epoch 30: val_accuracy did not improve from 0.61364

Epoch 31: val_accuracy did not improve from 0.61364

Epoch 32: val_accuracy did not improve from 0.61364

Epoch 33: val_accuracy did not improve from 0.61364

Epoch 34: val_accuracy did not improve from 0.61364

Epoch 35: val_accuracy did not improve from 0.61364
In [ ]:
mod1.summary()
Model: "sequential_14"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_37 (Dense)            (None, 13)                182       
                                                                 
 batch_normalization_18 (Ba  (None, 13)                52        
 tchNormalization)                                               
                                                                 
 dense_38 (Dense)            (None, 7)                 98        
                                                                 
 batch_normalization_19 (Ba  (None, 7)                 28        
 tchNormalization)                                               
                                                                 
 dense_39 (Dense)            (None, 2)                 16        
                                                                 
=================================================================
Total params: 376 (1.47 KB)
Trainable params: 336 (1.31 KB)
Non-trainable params: 40 (160.00 Byte)
_________________________________________________________________
In [ ]:
plot_history(history1)
In [ ]:
mod2 = keras.Sequential([
    keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
    keras.layers.Dense(8, activation='relu', kernel_initializer='he_uniform'),BatchNormalization(),
    #mean(13+2) = 7.5 ~ 7 or 8 for the number of neurons in the hidden layer
    keras.layers.Dense(2, activation='sigmoid')
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=20)
mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
lrate = 0.005
mod2.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate= lrate), metrics=['accuracy'])
history2 = mod2.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0,callbacks=[es, mc])
Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.70455

Epoch 4: val_accuracy did not improve from 0.70455

Epoch 5: val_accuracy did not improve from 0.70455

Epoch 6: val_accuracy did not improve from 0.70455

Epoch 7: val_accuracy did not improve from 0.70455

Epoch 8: val_accuracy did not improve from 0.70455

Epoch 9: val_accuracy did not improve from 0.70455

Epoch 10: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.72727

Epoch 12: val_accuracy did not improve from 0.72727

Epoch 13: val_accuracy did not improve from 0.72727

Epoch 14: val_accuracy did not improve from 0.72727

Epoch 15: val_accuracy did not improve from 0.72727

Epoch 16: val_accuracy did not improve from 0.72727

Epoch 17: val_accuracy did not improve from 0.72727

Epoch 18: val_accuracy did not improve from 0.72727

Epoch 19: val_accuracy did not improve from 0.72727

Epoch 20: val_accuracy did not improve from 0.72727

Epoch 21: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 22: val_accuracy did not improve from 0.75000

Epoch 23: val_accuracy did not improve from 0.75000

Epoch 24: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 25: val_accuracy did not improve from 0.77273

Epoch 26: val_accuracy did not improve from 0.77273

Epoch 27: val_accuracy did not improve from 0.77273

Epoch 28: val_accuracy did not improve from 0.77273

Epoch 29: val_accuracy did not improve from 0.77273

Epoch 30: val_accuracy did not improve from 0.77273

Epoch 31: val_accuracy did not improve from 0.77273

Epoch 32: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 33: val_accuracy did not improve from 0.79545

Epoch 34: val_accuracy did not improve from 0.79545

Epoch 35: val_accuracy did not improve from 0.79545

Epoch 36: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 37: val_accuracy did not improve from 0.81818

Epoch 38: val_accuracy did not improve from 0.81818

Epoch 39: val_accuracy did not improve from 0.81818

Epoch 40: val_accuracy did not improve from 0.81818

Epoch 41: val_accuracy did not improve from 0.81818

Epoch 42: val_accuracy did not improve from 0.81818

Epoch 43: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 44: val_accuracy did not improve from 0.84091

Epoch 45: val_accuracy did not improve from 0.84091

Epoch 46: val_accuracy did not improve from 0.84091

Epoch 47: val_accuracy did not improve from 0.84091

Epoch 48: val_accuracy did not improve from 0.84091

Epoch 49: val_accuracy did not improve from 0.84091

Epoch 50: val_accuracy did not improve from 0.84091

Epoch 51: val_accuracy did not improve from 0.84091

Epoch 52: val_accuracy did not improve from 0.84091

Epoch 53: val_accuracy did not improve from 0.84091

Epoch 54: val_accuracy did not improve from 0.84091

Epoch 55: val_accuracy did not improve from 0.84091

Epoch 56: val_accuracy did not improve from 0.84091

Epoch 57: val_accuracy did not improve from 0.84091

Epoch 58: val_accuracy did not improve from 0.84091

Epoch 59: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 60: val_accuracy did not improve from 0.86364

Epoch 61: val_accuracy did not improve from 0.86364

Epoch 62: val_accuracy did not improve from 0.86364

Epoch 63: val_accuracy did not improve from 0.86364

Epoch 64: val_accuracy did not improve from 0.86364

Epoch 65: val_accuracy did not improve from 0.86364

Epoch 66: val_accuracy did not improve from 0.86364

Epoch 67: val_accuracy did not improve from 0.86364

Epoch 68: val_accuracy did not improve from 0.86364

Epoch 69: val_accuracy did not improve from 0.86364

Epoch 70: val_accuracy did not improve from 0.86364

Epoch 71: val_accuracy did not improve from 0.86364

Epoch 72: val_accuracy did not improve from 0.86364

Epoch 73: val_accuracy did not improve from 0.86364

Epoch 74: val_accuracy did not improve from 0.86364

Epoch 75: val_accuracy did not improve from 0.86364

Epoch 76: val_accuracy did not improve from 0.86364

Epoch 77: val_accuracy did not improve from 0.86364

Epoch 78: val_accuracy did not improve from 0.86364

Epoch 79: val_accuracy did not improve from 0.86364
In [ ]:
mod2.summary()
Model: "sequential_15"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_40 (Dense)            (None, 13)                182       
                                                                 
 batch_normalization_20 (Ba  (None, 13)                52        
 tchNormalization)                                               
                                                                 
 dense_41 (Dense)            (None, 8)                 112       
                                                                 
 batch_normalization_21 (Ba  (None, 8)                 32        
 tchNormalization)                                               
                                                                 
 dense_42 (Dense)            (None, 2)                 18        
                                                                 
=================================================================
Total params: 396 (1.55 KB)
Trainable params: 354 (1.38 KB)
Non-trainable params: 42 (168.00 Byte)
_________________________________________________________________
In [ ]:
plot_history(history2)

As you can see the two models with hidden layers are overfitting. This is a result we could expect given that we did not have much data.

To try to have a good prediction we will optimize the first model. In the following part, we will test the different optimization methods.

In [ ]:
learning_rates = [1E-0, 1E-1, 1E-2, 1E-3,0.005,0.0025, 1E-4, 1E-5, 1E-6, 1E-7]
l2reg = [1,0.1,0.01,0.001,0.0001,0.0001]
classement =[]
ind=3
for k in range(len(l2reg)):
  for j,opti in enumerate(["adam","adagrad","SGD"]):
    for i in range(len(learning_rates)):
      print("\n\n#######################################################\n\n")
      print(f"the model mod{ind} use a learning rate = {i}, l2 regularization = {k} and the optimizer = {opti} :")
      try :
        globals()[f"mod{ind}"] = creation_model0(X_train,Y_train,X_test,Y_test,20,opti,learning_rates[i],l2reg[k])
        classement.append([globals()[f"mod{ind}"].evaluate(X_test,  Y_test, verbose=2)[1],f"mod{ind}"])

      except Exception as e:
        print(e)
      ind+=1

#######################################################


the model mod3 use a learning rate = 0, l2 regularization = 0 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.54545

Epoch 3: val_accuracy did not improve from 0.54545

Epoch 4: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.54545

Epoch 6: val_accuracy did not improve from 0.54545

Epoch 7: val_accuracy did not improve from 0.54545

Epoch 8: val_accuracy did not improve from 0.54545

Epoch 9: val_accuracy did not improve from 0.54545

Epoch 10: val_accuracy did not improve from 0.54545

Epoch 11: val_accuracy did not improve from 0.54545

Epoch 12: val_accuracy did not improve from 0.54545

Epoch 13: val_accuracy did not improve from 0.54545

Epoch 14: val_accuracy did not improve from 0.54545

Epoch 15: val_accuracy did not improve from 0.54545

Epoch 16: val_accuracy did not improve from 0.54545

Epoch 17: val_accuracy did not improve from 0.54545

Epoch 18: val_accuracy did not improve from 0.54545

Epoch 19: val_accuracy did not improve from 0.54545

Epoch 20: val_accuracy did not improve from 0.54545

Epoch 21: val_accuracy did not improve from 0.54545
2/2 - 0s - loss: 0.6909 - accuracy: 0.5455 - 24ms/epoch - 12ms/step


#######################################################


the model mod4 use a learning rate = 1, l2 regularization = 0 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.54545

Epoch 3: val_accuracy improved from 0.54545 to 0.72727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.72727 to 0.88636, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909

Epoch 35: val_accuracy did not improve from 0.90909

Epoch 36: val_accuracy did not improve from 0.90909

Epoch 37: val_accuracy did not improve from 0.90909

Epoch 38: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5070 - accuracy: 0.8636 - 88ms/epoch - 44ms/step


#######################################################


the model mod5 use a learning rate = 2, l2 regularization = 0 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.56818 to 0.65909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5

Epoch 4: val_accuracy improved from 0.68182 to 0.75000, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.77273

Epoch 7: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.79545

Epoch 9: val_accuracy did not improve from 0.79545

Epoch 10: val_accuracy did not improve from 0.79545

Epoch 11: val_accuracy did not improve from 0.79545

Epoch 12: val_accuracy did not improve from 0.79545

Epoch 13: val_accuracy did not improve from 0.79545

Epoch 14: val_accuracy did not improve from 0.79545

Epoch 15: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.81818

Epoch 17: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.84091

Epoch 19: val_accuracy did not improve from 0.84091

Epoch 20: val_accuracy did not improve from 0.84091

Epoch 21: val_accuracy did not improve from 0.84091

Epoch 22: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 23: val_accuracy did not improve from 0.86364

Epoch 24: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909

Epoch 35: val_accuracy did not improve from 0.90909

Epoch 36: val_accuracy did not improve from 0.90909

Epoch 37: val_accuracy did not improve from 0.90909

Epoch 38: val_accuracy did not improve from 0.90909

Epoch 39: val_accuracy did not improve from 0.90909

Epoch 40: val_accuracy did not improve from 0.90909

Epoch 41: val_accuracy did not improve from 0.90909

Epoch 42: val_accuracy did not improve from 0.90909

Epoch 43: val_accuracy did not improve from 0.90909

Epoch 44: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4113 - accuracy: 0.9091 - 23ms/epoch - 12ms/step


#######################################################


the model mod6 use a learning rate = 3, l2 regularization = 0 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.25000, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.25000

Epoch 3: val_accuracy did not improve from 0.25000

Epoch 4: val_accuracy did not improve from 0.25000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.25000

Epoch 6: val_accuracy did not improve from 0.25000

Epoch 7: val_accuracy improved from 0.25000 to 0.27273, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.27273

Epoch 9: val_accuracy improved from 0.27273 to 0.29545, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.29545

Epoch 11: val_accuracy improved from 0.29545 to 0.31818, saving model to best_model.h5

Epoch 12: val_accuracy improved from 0.31818 to 0.34091, saving model to best_model.h5

Epoch 13: val_accuracy improved from 0.34091 to 0.36364, saving model to best_model.h5

Epoch 14: val_accuracy improved from 0.36364 to 0.40909, saving model to best_model.h5

Epoch 15: val_accuracy did not improve from 0.40909

Epoch 16: val_accuracy did not improve from 0.40909

Epoch 17: val_accuracy did not improve from 0.40909

Epoch 18: val_accuracy did not improve from 0.40909

Epoch 19: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5

Epoch 20: val_accuracy did not improve from 0.43182

Epoch 21: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5

Epoch 22: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5

Epoch 23: val_accuracy did not improve from 0.47727

Epoch 24: val_accuracy did not improve from 0.47727

Epoch 25: val_accuracy did not improve from 0.47727

Epoch 26: val_accuracy did not improve from 0.47727

Epoch 27: val_accuracy did not improve from 0.47727

Epoch 28: val_accuracy did not improve from 0.47727

Epoch 29: val_accuracy did not improve from 0.47727

Epoch 30: val_accuracy improved from 0.47727 to 0.52273, saving model to best_model.h5

Epoch 31: val_accuracy did not improve from 0.52273

Epoch 32: val_accuracy improved from 0.52273 to 0.59091, saving model to best_model.h5

Epoch 33: val_accuracy did not improve from 0.59091

Epoch 34: val_accuracy improved from 0.59091 to 0.65909, saving model to best_model.h5

Epoch 35: val_accuracy improved from 0.65909 to 0.75000, saving model to best_model.h5

Epoch 36: val_accuracy did not improve from 0.75000

Epoch 37: val_accuracy did not improve from 0.75000

Epoch 38: val_accuracy did not improve from 0.75000

Epoch 39: val_accuracy did not improve from 0.75000

Epoch 40: val_accuracy did not improve from 0.75000

Epoch 41: val_accuracy did not improve from 0.75000

Epoch 42: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 43: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 44: val_accuracy did not improve from 0.79545

Epoch 45: val_accuracy did not improve from 0.79545

Epoch 46: val_accuracy did not improve from 0.79545

Epoch 47: val_accuracy did not improve from 0.79545

Epoch 48: val_accuracy did not improve from 0.79545

Epoch 49: val_accuracy did not improve from 0.79545

Epoch 50: val_accuracy did not improve from 0.79545

Epoch 51: val_accuracy did not improve from 0.79545

Epoch 52: val_accuracy did not improve from 0.79545

Epoch 53: val_accuracy did not improve from 0.79545

Epoch 54: val_accuracy did not improve from 0.79545

Epoch 55: val_accuracy did not improve from 0.79545

Epoch 56: val_accuracy did not improve from 0.79545

Epoch 57: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 58: val_accuracy did not improve from 0.81818

Epoch 59: val_accuracy did not improve from 0.81818

Epoch 60: val_accuracy did not improve from 0.81818

Epoch 61: val_accuracy did not improve from 0.81818

Epoch 62: val_accuracy did not improve from 0.81818

Epoch 63: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 64: val_accuracy did not improve from 0.84091

Epoch 65: val_accuracy did not improve from 0.84091

Epoch 66: val_accuracy did not improve from 0.84091

Epoch 67: val_accuracy did not improve from 0.84091

Epoch 68: val_accuracy did not improve from 0.84091

Epoch 69: val_accuracy did not improve from 0.84091

Epoch 70: val_accuracy did not improve from 0.84091

Epoch 71: val_accuracy did not improve from 0.84091

Epoch 72: val_accuracy did not improve from 0.84091

Epoch 73: val_accuracy did not improve from 0.84091

Epoch 74: val_accuracy did not improve from 0.84091

Epoch 75: val_accuracy did not improve from 0.84091

Epoch 76: val_accuracy did not improve from 0.84091

Epoch 77: val_accuracy did not improve from 0.84091

Epoch 78: val_accuracy did not improve from 0.84091

Epoch 79: val_accuracy did not improve from 0.84091

Epoch 80: val_accuracy did not improve from 0.84091

Epoch 81: val_accuracy did not improve from 0.84091

Epoch 82: val_accuracy did not improve from 0.84091

Epoch 83: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 2.8465 - accuracy: 0.7955 - 25ms/epoch - 13ms/step


#######################################################


the model mod7 use a learning rate = 4, l2 regularization = 0 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.61364, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.61364

Epoch 3: val_accuracy improved from 0.61364 to 0.68182, saving model to best_model.h5

Epoch 4: val_accuracy did not improve from 0.68182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.68182

Epoch 6: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.72727

Epoch 8: val_accuracy did not improve from 0.72727

Epoch 9: val_accuracy did not improve from 0.72727

Epoch 10: val_accuracy did not improve from 0.72727

Epoch 11: val_accuracy did not improve from 0.72727

Epoch 12: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.75000

Epoch 14: val_accuracy did not improve from 0.75000

Epoch 15: val_accuracy did not improve from 0.75000

Epoch 16: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 17: val_accuracy did not improve from 0.77273

Epoch 18: val_accuracy did not improve from 0.77273

Epoch 19: val_accuracy did not improve from 0.77273

Epoch 20: val_accuracy did not improve from 0.77273

Epoch 21: val_accuracy did not improve from 0.77273

Epoch 22: val_accuracy did not improve from 0.77273

Epoch 23: val_accuracy did not improve from 0.77273

Epoch 24: val_accuracy did not improve from 0.77273

Epoch 25: val_accuracy did not improve from 0.77273

Epoch 26: val_accuracy did not improve from 0.77273

Epoch 27: val_accuracy did not improve from 0.77273

Epoch 28: val_accuracy did not improve from 0.77273

Epoch 29: val_accuracy did not improve from 0.77273

Epoch 30: val_accuracy did not improve from 0.77273

Epoch 31: val_accuracy did not improve from 0.77273

Epoch 32: val_accuracy did not improve from 0.77273

Epoch 33: val_accuracy did not improve from 0.77273

Epoch 34: val_accuracy did not improve from 0.77273

Epoch 35: val_accuracy did not improve from 0.77273

Epoch 36: val_accuracy did not improve from 0.77273
2/2 - 0s - loss: 0.5710 - accuracy: 0.7727 - 38ms/epoch - 19ms/step


#######################################################


the model mod8 use a learning rate = 5, l2 regularization = 0 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.40909, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.40909

Epoch 3: val_accuracy did not improve from 0.40909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.40909

Epoch 5: val_accuracy did not improve from 0.40909

Epoch 6: val_accuracy improved from 0.40909 to 0.45455, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.45455

Epoch 8: val_accuracy did not improve from 0.45455

Epoch 9: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5

Epoch 10: val_accuracy improved from 0.47727 to 0.52273, saving model to best_model.h5

Epoch 11: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5

Epoch 12: val_accuracy improved from 0.56818 to 0.61364, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.61364

Epoch 14: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5

Epoch 15: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 16: val_accuracy improved from 0.65909 to 0.70455, saving model to best_model.h5

Epoch 17: val_accuracy did not improve from 0.70455

Epoch 18: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 19: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5

Epoch 20: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 21: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 22: val_accuracy did not improve from 0.81818

Epoch 23: val_accuracy did not improve from 0.81818

Epoch 24: val_accuracy did not improve from 0.81818

Epoch 25: val_accuracy did not improve from 0.81818

Epoch 26: val_accuracy did not improve from 0.81818

Epoch 27: val_accuracy did not improve from 0.81818

Epoch 28: val_accuracy did not improve from 0.81818

Epoch 29: val_accuracy did not improve from 0.81818

Epoch 30: val_accuracy did not improve from 0.81818

Epoch 31: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 32: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 33: val_accuracy did not improve from 0.86364

Epoch 34: val_accuracy did not improve from 0.86364

Epoch 35: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 36: val_accuracy did not improve from 0.88636

Epoch 37: val_accuracy did not improve from 0.88636

Epoch 38: val_accuracy improved from 0.88636 to 0.93182, saving model to best_model.h5

Epoch 39: val_accuracy did not improve from 0.93182

Epoch 40: val_accuracy did not improve from 0.93182

Epoch 41: val_accuracy did not improve from 0.93182

Epoch 42: val_accuracy did not improve from 0.93182

Epoch 43: val_accuracy did not improve from 0.93182

Epoch 44: val_accuracy did not improve from 0.93182

Epoch 45: val_accuracy did not improve from 0.93182

Epoch 46: val_accuracy did not improve from 0.93182

Epoch 47: val_accuracy did not improve from 0.93182

Epoch 48: val_accuracy did not improve from 0.93182

Epoch 49: val_accuracy did not improve from 0.93182

Epoch 50: val_accuracy did not improve from 0.93182

Epoch 51: val_accuracy did not improve from 0.93182

Epoch 52: val_accuracy did not improve from 0.93182

Epoch 53: val_accuracy did not improve from 0.93182

Epoch 54: val_accuracy did not improve from 0.93182

Epoch 55: val_accuracy did not improve from 0.93182

Epoch 56: val_accuracy did not improve from 0.93182

Epoch 57: val_accuracy did not improve from 0.93182

Epoch 58: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.7310 - accuracy: 0.8864 - 24ms/epoch - 12ms/step


#######################################################


the model mod9 use a learning rate = 6, l2 regularization = 0 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.70455

Epoch 3: val_accuracy did not improve from 0.70455

Epoch 4: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.72727

Epoch 6: val_accuracy did not improve from 0.72727

Epoch 7: val_accuracy did not improve from 0.72727

Epoch 8: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.75000

Epoch 10: val_accuracy did not improve from 0.75000

Epoch 11: val_accuracy did not improve from 0.75000

Epoch 12: val_accuracy did not improve from 0.75000

Epoch 13: val_accuracy did not improve from 0.75000

Epoch 14: val_accuracy did not improve from 0.75000

Epoch 15: val_accuracy did not improve from 0.75000

Epoch 16: val_accuracy did not improve from 0.75000

Epoch 17: val_accuracy did not improve from 0.75000

Epoch 18: val_accuracy did not improve from 0.75000

Epoch 19: val_accuracy did not improve from 0.75000

Epoch 20: val_accuracy did not improve from 0.75000

Epoch 21: val_accuracy did not improve from 0.75000

Epoch 22: val_accuracy did not improve from 0.75000

Epoch 23: val_accuracy did not improve from 0.75000

Epoch 24: val_accuracy did not improve from 0.75000

Epoch 25: val_accuracy did not improve from 0.75000

Epoch 26: val_accuracy did not improve from 0.75000

Epoch 27: val_accuracy did not improve from 0.75000

Epoch 28: val_accuracy did not improve from 0.75000
2/2 - 0s - loss: 25.4139 - accuracy: 0.7273 - 24ms/epoch - 12ms/step


#######################################################


the model mod10 use a learning rate = 7, l2 regularization = 0 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.70455

Epoch 3: val_accuracy did not improve from 0.70455

Epoch 4: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.70455

Epoch 6: val_accuracy did not improve from 0.70455

Epoch 7: val_accuracy did not improve from 0.70455

Epoch 8: val_accuracy did not improve from 0.70455

Epoch 9: val_accuracy did not improve from 0.70455

Epoch 10: val_accuracy did not improve from 0.70455

Epoch 11: val_accuracy did not improve from 0.70455

Epoch 12: val_accuracy did not improve from 0.70455

Epoch 13: val_accuracy did not improve from 0.70455

Epoch 14: val_accuracy did not improve from 0.70455

Epoch 15: val_accuracy did not improve from 0.70455

Epoch 16: val_accuracy did not improve from 0.70455

Epoch 17: val_accuracy did not improve from 0.70455

Epoch 18: val_accuracy did not improve from 0.70455

Epoch 19: val_accuracy did not improve from 0.70455

Epoch 20: val_accuracy did not improve from 0.70455

Epoch 21: val_accuracy did not improve from 0.70455
2/2 - 0s - loss: 27.0857 - accuracy: 0.7045 - 26ms/epoch - 13ms/step


#######################################################


the model mod11 use a learning rate = 8, l2 regularization = 0 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.68182

Epoch 3: val_accuracy did not improve from 0.68182

Epoch 4: val_accuracy did not improve from 0.68182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.68182

Epoch 6: val_accuracy did not improve from 0.68182

Epoch 7: val_accuracy did not improve from 0.68182

Epoch 8: val_accuracy did not improve from 0.68182

Epoch 9: val_accuracy did not improve from 0.68182

Epoch 10: val_accuracy did not improve from 0.68182

Epoch 11: val_accuracy did not improve from 0.68182

Epoch 12: val_accuracy did not improve from 0.68182

Epoch 13: val_accuracy did not improve from 0.68182

Epoch 14: val_accuracy did not improve from 0.68182

Epoch 15: val_accuracy did not improve from 0.68182

Epoch 16: val_accuracy did not improve from 0.68182

Epoch 17: val_accuracy did not improve from 0.68182

Epoch 18: val_accuracy did not improve from 0.68182

Epoch 19: val_accuracy did not improve from 0.68182

Epoch 20: val_accuracy did not improve from 0.68182

Epoch 21: val_accuracy did not improve from 0.68182
2/2 - 0s - loss: 29.3636 - accuracy: 0.6818 - 32ms/epoch - 16ms/step


#######################################################


the model mod12 use a learning rate = 9, l2 regularization = 0 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.47727, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.47727

Epoch 3: val_accuracy did not improve from 0.47727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.47727

Epoch 5: val_accuracy did not improve from 0.47727

Epoch 6: val_accuracy did not improve from 0.47727

Epoch 7: val_accuracy did not improve from 0.47727

Epoch 8: val_accuracy did not improve from 0.47727

Epoch 9: val_accuracy did not improve from 0.47727

Epoch 10: val_accuracy did not improve from 0.47727

Epoch 11: val_accuracy did not improve from 0.47727

Epoch 12: val_accuracy did not improve from 0.47727

Epoch 13: val_accuracy did not improve from 0.47727

Epoch 14: val_accuracy did not improve from 0.47727

Epoch 15: val_accuracy did not improve from 0.47727

Epoch 16: val_accuracy did not improve from 0.47727

Epoch 17: val_accuracy did not improve from 0.47727

Epoch 18: val_accuracy did not improve from 0.47727

Epoch 19: val_accuracy did not improve from 0.47727

Epoch 20: val_accuracy did not improve from 0.47727

Epoch 21: val_accuracy did not improve from 0.47727
2/2 - 0s - loss: 26.1684 - accuracy: 0.4773 - 26ms/epoch - 13ms/step


#######################################################


the model mod13 use a learning rate = 0, l2 regularization = 0 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.84091

Epoch 3: val_accuracy did not improve from 0.84091

Epoch 4: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.84091

Epoch 6: val_accuracy did not improve from 0.84091

Epoch 7: val_accuracy did not improve from 0.84091

Epoch 8: val_accuracy did not improve from 0.84091

Epoch 9: val_accuracy did not improve from 0.84091

Epoch 10: val_accuracy did not improve from 0.84091

Epoch 11: val_accuracy did not improve from 0.84091

Epoch 12: val_accuracy did not improve from 0.84091

Epoch 13: val_accuracy did not improve from 0.84091

Epoch 14: val_accuracy did not improve from 0.84091

Epoch 15: val_accuracy did not improve from 0.84091

Epoch 16: val_accuracy did not improve from 0.84091

Epoch 17: val_accuracy did not improve from 0.84091

Epoch 18: val_accuracy did not improve from 0.84091

Epoch 19: val_accuracy did not improve from 0.84091

Epoch 20: val_accuracy did not improve from 0.84091

Epoch 21: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.9606 - accuracy: 0.5000 - 25ms/epoch - 12ms/step


#######################################################


the model mod14 use a learning rate = 1, l2 regularization = 0 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.52273, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.56818

Epoch 4: val_accuracy did not improve from 0.56818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy improved from 0.56818 to 0.61364, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.61364 to 0.72727, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.72727

Epoch 8: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.75000

Epoch 10: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.77273

Epoch 12: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 13: val_accuracy improved from 0.79545 to 0.84091, saving model to best_model.h5

Epoch 14: val_accuracy did not improve from 0.84091

Epoch 15: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 16: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909

Epoch 35: val_accuracy did not improve from 0.90909

Epoch 36: val_accuracy did not improve from 0.90909

Epoch 37: val_accuracy did not improve from 0.90909

Epoch 38: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4648 - accuracy: 0.8864 - 24ms/epoch - 12ms/step


#######################################################


the model mod15 use a learning rate = 2, l2 regularization = 0 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.59091

Epoch 4: val_accuracy did not improve from 0.59091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.61364

Epoch 7: val_accuracy did not improve from 0.61364

Epoch 8: val_accuracy did not improve from 0.61364

Epoch 9: val_accuracy did not improve from 0.61364

Epoch 10: val_accuracy did not improve from 0.61364

Epoch 11: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.63636

Epoch 13: val_accuracy did not improve from 0.63636

Epoch 14: val_accuracy improved from 0.63636 to 0.68182, saving model to best_model.h5

Epoch 15: val_accuracy did not improve from 0.68182

Epoch 16: val_accuracy did not improve from 0.68182

Epoch 17: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.72727

Epoch 19: val_accuracy did not improve from 0.72727

Epoch 20: val_accuracy did not improve from 0.72727

Epoch 21: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 22: val_accuracy did not improve from 0.75000

Epoch 23: val_accuracy did not improve from 0.75000

Epoch 24: val_accuracy did not improve from 0.75000

Epoch 25: val_accuracy did not improve from 0.75000

Epoch 26: val_accuracy did not improve from 0.75000

Epoch 27: val_accuracy did not improve from 0.75000

Epoch 28: val_accuracy did not improve from 0.75000

Epoch 29: val_accuracy did not improve from 0.75000

Epoch 30: val_accuracy did not improve from 0.75000

Epoch 31: val_accuracy did not improve from 0.75000

Epoch 32: val_accuracy did not improve from 0.75000

Epoch 33: val_accuracy did not improve from 0.75000

Epoch 34: val_accuracy did not improve from 0.75000

Epoch 35: val_accuracy did not improve from 0.75000

Epoch 36: val_accuracy did not improve from 0.75000

Epoch 37: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 38: val_accuracy did not improve from 0.77273

Epoch 39: val_accuracy did not improve from 0.77273

Epoch 40: val_accuracy did not improve from 0.77273

Epoch 41: val_accuracy did not improve from 0.77273

Epoch 42: val_accuracy improved from 0.77273 to 0.81818, saving model to best_model.h5

Epoch 43: val_accuracy did not improve from 0.81818

Epoch 44: val_accuracy did not improve from 0.81818

Epoch 45: val_accuracy did not improve from 0.81818

Epoch 46: val_accuracy did not improve from 0.81818

Epoch 47: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 48: val_accuracy did not improve from 0.84091

Epoch 49: val_accuracy did not improve from 0.84091

Epoch 50: val_accuracy did not improve from 0.84091

Epoch 51: val_accuracy did not improve from 0.84091

Epoch 52: val_accuracy did not improve from 0.84091

Epoch 53: val_accuracy did not improve from 0.84091

Epoch 54: val_accuracy did not improve from 0.84091

Epoch 55: val_accuracy did not improve from 0.84091

Epoch 56: val_accuracy did not improve from 0.84091

Epoch 57: val_accuracy did not improve from 0.84091

Epoch 58: val_accuracy did not improve from 0.84091

Epoch 59: val_accuracy did not improve from 0.84091

Epoch 60: val_accuracy did not improve from 0.84091

Epoch 61: val_accuracy did not improve from 0.84091

Epoch 62: val_accuracy did not improve from 0.84091

Epoch 63: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 64: val_accuracy did not improve from 0.86364

Epoch 65: val_accuracy did not improve from 0.86364

Epoch 66: val_accuracy did not improve from 0.86364

Epoch 67: val_accuracy did not improve from 0.86364

Epoch 68: val_accuracy did not improve from 0.86364

Epoch 69: val_accuracy did not improve from 0.86364

Epoch 70: val_accuracy did not improve from 0.86364

Epoch 71: val_accuracy did not improve from 0.86364

Epoch 72: val_accuracy did not improve from 0.86364

Epoch 73: val_accuracy did not improve from 0.86364

Epoch 74: val_accuracy did not improve from 0.86364

Epoch 75: val_accuracy did not improve from 0.86364

Epoch 76: val_accuracy did not improve from 0.86364

Epoch 77: val_accuracy did not improve from 0.86364

Epoch 78: val_accuracy did not improve from 0.86364

Epoch 79: val_accuracy did not improve from 0.86364

Epoch 80: val_accuracy did not improve from 0.86364

Epoch 81: val_accuracy did not improve from 0.86364

Epoch 82: val_accuracy did not improve from 0.86364

Epoch 83: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 3.4218 - accuracy: 0.8636 - 27ms/epoch - 14ms/step


#######################################################


the model mod16 use a learning rate = 3, l2 regularization = 0 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.68182

Epoch 3: val_accuracy did not improve from 0.68182

Epoch 4: val_accuracy did not improve from 0.68182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.68182

Epoch 6: val_accuracy did not improve from 0.68182

Epoch 7: val_accuracy did not improve from 0.68182

Epoch 8: val_accuracy did not improve from 0.68182

Epoch 9: val_accuracy did not improve from 0.68182

Epoch 10: val_accuracy did not improve from 0.68182

Epoch 11: val_accuracy did not improve from 0.68182

Epoch 12: val_accuracy did not improve from 0.68182

Epoch 13: val_accuracy did not improve from 0.68182

Epoch 14: val_accuracy did not improve from 0.68182

Epoch 15: val_accuracy did not improve from 0.68182

Epoch 16: val_accuracy did not improve from 0.68182

Epoch 17: val_accuracy did not improve from 0.68182

Epoch 18: val_accuracy did not improve from 0.68182

Epoch 19: val_accuracy did not improve from 0.68182

Epoch 20: val_accuracy did not improve from 0.68182

Epoch 21: val_accuracy did not improve from 0.68182
2/2 - 0s - loss: 25.0937 - accuracy: 0.6818 - 35ms/epoch - 18ms/step


#######################################################


the model mod17 use a learning rate = 4, l2 regularization = 0 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.70455

Epoch 3: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.70455

Epoch 5: val_accuracy did not improve from 0.70455

Epoch 6: val_accuracy did not improve from 0.70455

Epoch 7: val_accuracy improved from 0.70455 to 0.75000, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.77273

Epoch 10: val_accuracy did not improve from 0.77273

Epoch 11: val_accuracy did not improve from 0.77273

Epoch 12: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.79545

Epoch 14: val_accuracy did not improve from 0.79545

Epoch 15: val_accuracy did not improve from 0.79545

Epoch 16: val_accuracy did not improve from 0.79545

Epoch 17: val_accuracy did not improve from 0.79545

Epoch 18: val_accuracy did not improve from 0.79545

Epoch 19: val_accuracy did not improve from 0.79545

Epoch 20: val_accuracy did not improve from 0.79545

Epoch 21: val_accuracy did not improve from 0.79545

Epoch 22: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 23: val_accuracy did not improve from 0.81818

Epoch 24: val_accuracy did not improve from 0.81818

Epoch 25: val_accuracy did not improve from 0.81818

Epoch 26: val_accuracy did not improve from 0.81818

Epoch 27: val_accuracy did not improve from 0.81818

Epoch 28: val_accuracy did not improve from 0.81818

Epoch 29: val_accuracy did not improve from 0.81818

Epoch 30: val_accuracy did not improve from 0.81818

Epoch 31: val_accuracy did not improve from 0.81818

Epoch 32: val_accuracy did not improve from 0.81818

Epoch 33: val_accuracy did not improve from 0.81818

Epoch 34: val_accuracy did not improve from 0.81818

Epoch 35: val_accuracy did not improve from 0.81818

Epoch 36: val_accuracy did not improve from 0.81818

Epoch 37: val_accuracy did not improve from 0.81818

Epoch 38: val_accuracy did not improve from 0.81818

Epoch 39: val_accuracy did not improve from 0.81818

Epoch 40: val_accuracy did not improve from 0.81818

Epoch 41: val_accuracy did not improve from 0.81818

Epoch 42: val_accuracy did not improve from 0.81818
2/2 - 0s - loss: 13.3420 - accuracy: 0.8182 - 24ms/epoch - 12ms/step


#######################################################


the model mod18 use a learning rate = 5, l2 regularization = 0 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.47727, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.50000

Epoch 4: val_accuracy did not improve from 0.50000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.50000

Epoch 6: val_accuracy did not improve from 0.50000

Epoch 7: val_accuracy did not improve from 0.50000

Epoch 8: val_accuracy did not improve from 0.50000

Epoch 9: val_accuracy did not improve from 0.50000

Epoch 10: val_accuracy did not improve from 0.50000

Epoch 11: val_accuracy did not improve from 0.50000

Epoch 12: val_accuracy did not improve from 0.50000

Epoch 13: val_accuracy did not improve from 0.50000

Epoch 14: val_accuracy did not improve from 0.50000

Epoch 15: val_accuracy did not improve from 0.50000

Epoch 16: val_accuracy did not improve from 0.50000

Epoch 17: val_accuracy did not improve from 0.50000

Epoch 18: val_accuracy did not improve from 0.50000

Epoch 19: val_accuracy did not improve from 0.50000

Epoch 20: val_accuracy did not improve from 0.50000

Epoch 21: val_accuracy did not improve from 0.50000

Epoch 22: val_accuracy did not improve from 0.50000
2/2 - 0s - loss: 22.2522 - accuracy: 0.5000 - 25ms/epoch - 13ms/step


#######################################################


the model mod19 use a learning rate = 6, l2 regularization = 0 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.54545

Epoch 3: val_accuracy did not improve from 0.54545

Epoch 4: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.54545

Epoch 6: val_accuracy did not improve from 0.54545

Epoch 7: val_accuracy did not improve from 0.54545

Epoch 8: val_accuracy did not improve from 0.54545

Epoch 9: val_accuracy did not improve from 0.54545

Epoch 10: val_accuracy did not improve from 0.54545

Epoch 11: val_accuracy did not improve from 0.54545

Epoch 12: val_accuracy did not improve from 0.54545

Epoch 13: val_accuracy did not improve from 0.54545

Epoch 14: val_accuracy did not improve from 0.54545

Epoch 15: val_accuracy did not improve from 0.54545

Epoch 16: val_accuracy did not improve from 0.54545

Epoch 17: val_accuracy did not improve from 0.54545

Epoch 18: val_accuracy did not improve from 0.54545

Epoch 19: val_accuracy did not improve from 0.54545

Epoch 20: val_accuracy did not improve from 0.54545

Epoch 21: val_accuracy did not improve from 0.54545
2/2 - 0s - loss: 28.3889 - accuracy: 0.5455 - 25ms/epoch - 12ms/step


#######################################################


the model mod20 use a learning rate = 7, l2 regularization = 0 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.56818

Epoch 3: val_accuracy did not improve from 0.56818

Epoch 4: val_accuracy did not improve from 0.56818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.56818

Epoch 6: val_accuracy did not improve from 0.56818

Epoch 7: val_accuracy did not improve from 0.56818

Epoch 8: val_accuracy did not improve from 0.56818

Epoch 9: val_accuracy did not improve from 0.56818

Epoch 10: val_accuracy did not improve from 0.56818

Epoch 11: val_accuracy did not improve from 0.56818

Epoch 12: val_accuracy did not improve from 0.56818

Epoch 13: val_accuracy did not improve from 0.56818

Epoch 14: val_accuracy did not improve from 0.56818

Epoch 15: val_accuracy did not improve from 0.56818

Epoch 16: val_accuracy did not improve from 0.56818

Epoch 17: val_accuracy did not improve from 0.56818

Epoch 18: val_accuracy did not improve from 0.56818

Epoch 19: val_accuracy did not improve from 0.56818

Epoch 20: val_accuracy did not improve from 0.56818

Epoch 21: val_accuracy did not improve from 0.56818
2/2 - 0s - loss: 28.6149 - accuracy: 0.5682 - 25ms/epoch - 13ms/step


#######################################################


the model mod21 use a learning rate = 8, l2 regularization = 0 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.36364

Epoch 3: val_accuracy did not improve from 0.36364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.36364

Epoch 5: val_accuracy did not improve from 0.36364

Epoch 6: val_accuracy did not improve from 0.36364

Epoch 7: val_accuracy did not improve from 0.36364

Epoch 8: val_accuracy did not improve from 0.36364

Epoch 9: val_accuracy did not improve from 0.36364

Epoch 10: val_accuracy did not improve from 0.36364

Epoch 11: val_accuracy did not improve from 0.36364

Epoch 12: val_accuracy did not improve from 0.36364

Epoch 13: val_accuracy did not improve from 0.36364

Epoch 14: val_accuracy did not improve from 0.36364

Epoch 15: val_accuracy did not improve from 0.36364

Epoch 16: val_accuracy did not improve from 0.36364

Epoch 17: val_accuracy did not improve from 0.36364

Epoch 18: val_accuracy did not improve from 0.36364

Epoch 19: val_accuracy did not improve from 0.36364

Epoch 20: val_accuracy did not improve from 0.36364

Epoch 21: val_accuracy did not improve from 0.36364
2/2 - 0s - loss: 27.6002 - accuracy: 0.3636 - 31ms/epoch - 15ms/step


#######################################################


the model mod22 use a learning rate = 9, l2 regularization = 0 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.54545

Epoch 3: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.54545

Epoch 5: val_accuracy did not improve from 0.54545

Epoch 6: val_accuracy did not improve from 0.54545

Epoch 7: val_accuracy did not improve from 0.54545

Epoch 8: val_accuracy did not improve from 0.54545

Epoch 9: val_accuracy did not improve from 0.54545

Epoch 10: val_accuracy did not improve from 0.54545

Epoch 11: val_accuracy did not improve from 0.54545

Epoch 12: val_accuracy did not improve from 0.54545

Epoch 13: val_accuracy did not improve from 0.54545

Epoch 14: val_accuracy did not improve from 0.54545

Epoch 15: val_accuracy did not improve from 0.54545

Epoch 16: val_accuracy did not improve from 0.54545

Epoch 17: val_accuracy did not improve from 0.54545

Epoch 18: val_accuracy did not improve from 0.54545

Epoch 19: val_accuracy did not improve from 0.54545

Epoch 20: val_accuracy did not improve from 0.54545

Epoch 21: val_accuracy did not improve from 0.54545
2/2 - 0s - loss: 26.6883 - accuracy: 0.5455 - 23ms/epoch - 11ms/step


#######################################################


the model mod23 use a learning rate = 0, l2 regularization = 0 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.36364 to 0.65909, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.65909

Epoch 4: val_accuracy did not improve from 0.65909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy improved from 0.65909 to 0.77273, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.77273

Epoch 7: val_accuracy improved from 0.77273 to 0.81818, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.81818

Epoch 9: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 10: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909

Epoch 35: val_accuracy did not improve from 0.90909

Epoch 36: val_accuracy did not improve from 0.90909

Epoch 37: val_accuracy did not improve from 0.90909

Epoch 38: val_accuracy did not improve from 0.90909

Epoch 39: val_accuracy did not improve from 0.90909

Epoch 40: val_accuracy did not improve from 0.90909

Epoch 41: val_accuracy did not improve from 0.90909

Epoch 42: val_accuracy did not improve from 0.90909

Epoch 43: val_accuracy did not improve from 0.90909

Epoch 44: val_accuracy did not improve from 0.90909

Epoch 45: val_accuracy did not improve from 0.90909

Epoch 46: val_accuracy did not improve from 0.90909

Epoch 47: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.7387 - accuracy: 0.6364 - 44ms/epoch - 22ms/step


#######################################################


the model mod24 use a learning rate = 1, l2 regularization = 0 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.29545, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.29545 to 0.65909, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.65909

Epoch 4: val_accuracy did not improve from 0.65909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy improved from 0.65909 to 0.72727, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.72727 to 0.84091, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.84091

Epoch 8: val_accuracy did not improve from 0.84091

Epoch 9: val_accuracy did not improve from 0.84091

Epoch 10: val_accuracy did not improve from 0.84091

Epoch 11: val_accuracy improved from 0.84091 to 0.90909, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5599 - accuracy: 0.8182 - 24ms/epoch - 12ms/step


#######################################################


the model mod25 use a learning rate = 2, l2 regularization = 0 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.56818

Epoch 3: val_accuracy improved from 0.56818 to 0.68182, saving model to best_model.h5

Epoch 4: val_accuracy did not improve from 0.68182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.68182

Epoch 6: val_accuracy did not improve from 0.68182

Epoch 7: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.70455 to 0.75000, saving model to best_model.h5

Epoch 9: val_accuracy improved from 0.75000 to 0.79545, saving model to best_model.h5

Epoch 10: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.81818

Epoch 12: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4932 - accuracy: 0.8864 - 25ms/epoch - 13ms/step


#######################################################


the model mod26 use a learning rate = 3, l2 regularization = 0 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.50000 to 0.54545, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.56818

Epoch 5: val_accuracy improved from 0.56818 to 0.72727, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.72727 to 0.84091, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy did not improve from 0.88636

Epoch 28: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5983 - accuracy: 0.8636 - 36ms/epoch - 18ms/step


#######################################################


the model mod27 use a learning rate = 4, l2 regularization = 0 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.63636, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.63636 to 0.72727, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.72727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.72727

Epoch 5: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.77273 to 0.86364, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5402 - accuracy: 0.8182 - 23ms/epoch - 12ms/step


#######################################################


the model mod28 use a learning rate = 5, l2 regularization = 0 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.59091

Epoch 3: val_accuracy did not improve from 0.59091

Epoch 4: val_accuracy did not improve from 0.59091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.59091

Epoch 6: val_accuracy did not improve from 0.59091

Epoch 7: val_accuracy improved from 0.59091 to 0.65909, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.65909 to 0.77273, saving model to best_model.h5

Epoch 9: val_accuracy improved from 0.77273 to 0.84091, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.84091

Epoch 11: val_accuracy did not improve from 0.84091

Epoch 12: val_accuracy improved from 0.84091 to 0.90909, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5508 - accuracy: 0.9091 - 24ms/epoch - 12ms/step


#######################################################


the model mod29 use a learning rate = 6, l2 regularization = 0 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.59091 to 0.63636, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.63636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.63636 to 0.75000, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.75000

Epoch 6: val_accuracy did not improve from 0.75000

Epoch 7: val_accuracy did not improve from 0.75000

Epoch 8: val_accuracy improved from 0.75000 to 0.84091, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.84091

Epoch 10: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909

Epoch 35: val_accuracy did not improve from 0.90909

Epoch 36: val_accuracy did not improve from 0.90909

Epoch 37: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5458 - accuracy: 0.7955 - 36ms/epoch - 18ms/step


#######################################################


the model mod30 use a learning rate = 7, l2 regularization = 0 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.65909, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.65909

Epoch 3: val_accuracy did not improve from 0.65909

Epoch 4: val_accuracy did not improve from 0.65909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.65909

Epoch 6: val_accuracy improved from 0.65909 to 0.75000, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.75000

Epoch 8: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.77273

Epoch 10: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 11: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5954 - accuracy: 0.8182 - 24ms/epoch - 12ms/step


#######################################################


the model mod31 use a learning rate = 8, l2 regularization = 0 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.63636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.63636

Epoch 3: val_accuracy did not improve from 0.63636

Epoch 4: val_accuracy did not improve from 0.63636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy improved from 0.63636 to 0.72727, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.72727 to 0.81818, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.81818

Epoch 8: val_accuracy did not improve from 0.81818

Epoch 9: val_accuracy did not improve from 0.81818

Epoch 10: val_accuracy did not improve from 0.81818

Epoch 11: val_accuracy did not improve from 0.81818

Epoch 12: val_accuracy improved from 0.81818 to 0.86364, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364

Epoch 23: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy did not improve from 0.88636

Epoch 28: val_accuracy did not improve from 0.88636

Epoch 29: val_accuracy did not improve from 0.88636

Epoch 30: val_accuracy did not improve from 0.88636

Epoch 31: val_accuracy did not improve from 0.88636

Epoch 32: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909

Epoch 35: val_accuracy did not improve from 0.90909

Epoch 36: val_accuracy did not improve from 0.90909

Epoch 37: val_accuracy did not improve from 0.90909

Epoch 38: val_accuracy did not improve from 0.90909

Epoch 39: val_accuracy did not improve from 0.90909

Epoch 40: val_accuracy did not improve from 0.90909

Epoch 41: val_accuracy did not improve from 0.90909

Epoch 42: val_accuracy did not improve from 0.90909

Epoch 43: val_accuracy did not improve from 0.90909

Epoch 44: val_accuracy did not improve from 0.90909

Epoch 45: val_accuracy did not improve from 0.90909

Epoch 46: val_accuracy did not improve from 0.90909

Epoch 47: val_accuracy did not improve from 0.90909

Epoch 48: val_accuracy did not improve from 0.90909

Epoch 49: val_accuracy did not improve from 0.90909

Epoch 50: val_accuracy did not improve from 0.90909

Epoch 51: val_accuracy did not improve from 0.90909

Epoch 52: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5716 - accuracy: 0.8409 - 55ms/epoch - 28ms/step


#######################################################


the model mod32 use a learning rate = 9, l2 regularization = 0 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.70455

Epoch 3: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.70455

Epoch 5: val_accuracy did not improve from 0.70455

Epoch 6: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.77273 to 0.86364, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy improved from 0.90909 to 0.95455, saving model to best_model.h5

Epoch 28: val_accuracy did not improve from 0.95455

Epoch 29: val_accuracy did not improve from 0.95455

Epoch 30: val_accuracy did not improve from 0.95455

Epoch 31: val_accuracy did not improve from 0.95455

Epoch 32: val_accuracy did not improve from 0.95455

Epoch 33: val_accuracy did not improve from 0.95455

Epoch 34: val_accuracy did not improve from 0.95455

Epoch 35: val_accuracy did not improve from 0.95455

Epoch 36: val_accuracy did not improve from 0.95455

Epoch 37: val_accuracy did not improve from 0.95455

Epoch 38: val_accuracy did not improve from 0.95455

Epoch 39: val_accuracy did not improve from 0.95455

Epoch 40: val_accuracy did not improve from 0.95455

Epoch 41: val_accuracy did not improve from 0.95455

Epoch 42: val_accuracy did not improve from 0.95455

Epoch 43: val_accuracy did not improve from 0.95455

Epoch 44: val_accuracy did not improve from 0.95455

Epoch 45: val_accuracy did not improve from 0.95455

Epoch 46: val_accuracy did not improve from 0.95455

Epoch 47: val_accuracy did not improve from 0.95455
2/2 - 0s - loss: 0.5557 - accuracy: 0.8182 - 49ms/epoch - 25ms/step


#######################################################


the model mod33 use a learning rate = 0, l2 regularization = 1 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.50000

Epoch 3: val_accuracy improved from 0.50000 to 0.72727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.72727 to 0.86364, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.86364

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy did not improve from 0.88636

Epoch 28: val_accuracy did not improve from 0.88636

Epoch 29: val_accuracy did not improve from 0.88636

Epoch 30: val_accuracy did not improve from 0.88636

Epoch 31: val_accuracy did not improve from 0.88636

Epoch 32: val_accuracy did not improve from 0.88636

Epoch 33: val_accuracy did not improve from 0.88636

Epoch 34: val_accuracy did not improve from 0.88636

Epoch 35: val_accuracy did not improve from 0.88636

Epoch 36: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.6896 - accuracy: 0.5455 - 23ms/epoch - 11ms/step


#######################################################


the model mod34 use a learning rate = 1, l2 regularization = 1 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.70455 to 0.81818, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4313 - accuracy: 0.8182 - 37ms/epoch - 18ms/step


#######################################################


the model mod35 use a learning rate = 2, l2 regularization = 1 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.50000 to 0.75000, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.75000 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.86364

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909

Epoch 35: val_accuracy did not improve from 0.90909

Epoch 36: val_accuracy did not improve from 0.90909

Epoch 37: val_accuracy did not improve from 0.90909

Epoch 38: val_accuracy did not improve from 0.90909

Epoch 39: val_accuracy did not improve from 0.90909

Epoch 40: val_accuracy did not improve from 0.90909

Epoch 41: val_accuracy did not improve from 0.90909

Epoch 42: val_accuracy did not improve from 0.90909

Epoch 43: val_accuracy did not improve from 0.90909

Epoch 44: val_accuracy did not improve from 0.90909

Epoch 45: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3520 - accuracy: 0.8864 - 24ms/epoch - 12ms/step


#######################################################


the model mod36 use a learning rate = 3, l2 regularization = 1 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.70455

Epoch 5: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.79545

Epoch 9: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.81818

Epoch 11: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.84091

Epoch 13: val_accuracy did not improve from 0.84091

Epoch 14: val_accuracy did not improve from 0.84091

Epoch 15: val_accuracy did not improve from 0.84091

Epoch 16: val_accuracy did not improve from 0.84091

Epoch 17: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy did not improve from 0.88636

Epoch 28: val_accuracy did not improve from 0.88636

Epoch 29: val_accuracy did not improve from 0.88636

Epoch 30: val_accuracy did not improve from 0.88636

Epoch 31: val_accuracy did not improve from 0.88636

Epoch 32: val_accuracy did not improve from 0.88636

Epoch 33: val_accuracy did not improve from 0.88636

Epoch 34: val_accuracy did not improve from 0.88636

Epoch 35: val_accuracy did not improve from 0.88636

Epoch 36: val_accuracy did not improve from 0.88636

Epoch 37: val_accuracy did not improve from 0.88636

Epoch 38: val_accuracy did not improve from 0.88636

Epoch 39: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 1.3347 - accuracy: 0.8864 - 24ms/epoch - 12ms/step


#######################################################


the model mod37 use a learning rate = 4, l2 regularization = 1 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.52273, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.52273 to 0.59091, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.59091 to 0.72727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.75000 to 0.79545, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.79545

Epoch 7: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 9: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909

Epoch 35: val_accuracy did not improve from 0.90909

Epoch 36: val_accuracy did not improve from 0.90909

Epoch 37: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3590 - accuracy: 0.8864 - 116ms/epoch - 58ms/step


#######################################################


the model mod38 use a learning rate = 5, l2 regularization = 1 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.45455, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.47727 to 0.54545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.61364

Epoch 8: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.63636

Epoch 10: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 11: val_accuracy improved from 0.65909 to 0.70455, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.70455

Epoch 13: val_accuracy did not improve from 0.70455

Epoch 14: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 15: val_accuracy did not improve from 0.72727

Epoch 16: val_accuracy did not improve from 0.72727

Epoch 17: val_accuracy did not improve from 0.72727

Epoch 18: val_accuracy did not improve from 0.72727

Epoch 19: val_accuracy did not improve from 0.72727

Epoch 20: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 21: val_accuracy did not improve from 0.75000

Epoch 22: val_accuracy did not improve from 0.75000

Epoch 23: val_accuracy did not improve from 0.75000

Epoch 24: val_accuracy did not improve from 0.75000

Epoch 25: val_accuracy did not improve from 0.75000

Epoch 26: val_accuracy did not improve from 0.75000

Epoch 27: val_accuracy did not improve from 0.75000

Epoch 28: val_accuracy did not improve from 0.75000

Epoch 29: val_accuracy did not improve from 0.75000

Epoch 30: val_accuracy did not improve from 0.75000

Epoch 31: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 32: val_accuracy did not improve from 0.77273

Epoch 33: val_accuracy did not improve from 0.77273

Epoch 34: val_accuracy did not improve from 0.77273

Epoch 35: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 36: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 37: val_accuracy did not improve from 0.81818

Epoch 38: val_accuracy did not improve from 0.81818

Epoch 39: val_accuracy did not improve from 0.81818

Epoch 40: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 41: val_accuracy did not improve from 0.84091

Epoch 42: val_accuracy did not improve from 0.84091

Epoch 43: val_accuracy did not improve from 0.84091

Epoch 44: val_accuracy did not improve from 0.84091

Epoch 45: val_accuracy did not improve from 0.84091

Epoch 46: val_accuracy did not improve from 0.84091

Epoch 47: val_accuracy did not improve from 0.84091

Epoch 48: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 49: val_accuracy did not improve from 0.86364

Epoch 50: val_accuracy did not improve from 0.86364

Epoch 51: val_accuracy did not improve from 0.86364

Epoch 52: val_accuracy did not improve from 0.86364

Epoch 53: val_accuracy did not improve from 0.86364

Epoch 54: val_accuracy did not improve from 0.86364

Epoch 55: val_accuracy did not improve from 0.86364

Epoch 56: val_accuracy did not improve from 0.86364

Epoch 57: val_accuracy did not improve from 0.86364

Epoch 58: val_accuracy did not improve from 0.86364

Epoch 59: val_accuracy did not improve from 0.86364

Epoch 60: val_accuracy did not improve from 0.86364

Epoch 61: val_accuracy did not improve from 0.86364

Epoch 62: val_accuracy did not improve from 0.86364

Epoch 63: val_accuracy did not improve from 0.86364

Epoch 64: val_accuracy did not improve from 0.86364

Epoch 65: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 66: val_accuracy did not improve from 0.88636

Epoch 67: val_accuracy did not improve from 0.88636

Epoch 68: val_accuracy did not improve from 0.88636

Epoch 69: val_accuracy did not improve from 0.88636

Epoch 70: val_accuracy did not improve from 0.88636

Epoch 71: val_accuracy did not improve from 0.88636

Epoch 72: val_accuracy did not improve from 0.88636

Epoch 73: val_accuracy did not improve from 0.88636

Epoch 74: val_accuracy did not improve from 0.88636

Epoch 75: val_accuracy did not improve from 0.88636

Epoch 76: val_accuracy did not improve from 0.88636

Epoch 77: val_accuracy did not improve from 0.88636

Epoch 78: val_accuracy did not improve from 0.88636

Epoch 79: val_accuracy did not improve from 0.88636

Epoch 80: val_accuracy did not improve from 0.88636

Epoch 81: val_accuracy did not improve from 0.88636

Epoch 82: val_accuracy did not improve from 0.88636

Epoch 83: val_accuracy did not improve from 0.88636

Epoch 84: val_accuracy did not improve from 0.88636

Epoch 85: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.3546 - accuracy: 0.8864 - 34ms/epoch - 17ms/step


#######################################################


the model mod39 use a learning rate = 6, l2 regularization = 1 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.18182, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.18182

Epoch 3: val_accuracy did not improve from 0.18182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.18182

Epoch 5: val_accuracy did not improve from 0.18182

Epoch 6: val_accuracy did not improve from 0.18182

Epoch 7: val_accuracy did not improve from 0.18182

Epoch 8: val_accuracy did not improve from 0.18182

Epoch 9: val_accuracy did not improve from 0.18182

Epoch 10: val_accuracy did not improve from 0.18182

Epoch 11: val_accuracy did not improve from 0.18182

Epoch 12: val_accuracy did not improve from 0.18182

Epoch 13: val_accuracy did not improve from 0.18182

Epoch 14: val_accuracy did not improve from 0.18182

Epoch 15: val_accuracy did not improve from 0.18182

Epoch 16: val_accuracy did not improve from 0.18182

Epoch 17: val_accuracy did not improve from 0.18182

Epoch 18: val_accuracy did not improve from 0.18182

Epoch 19: val_accuracy did not improve from 0.18182

Epoch 20: val_accuracy did not improve from 0.18182

Epoch 21: val_accuracy did not improve from 0.18182
2/2 - 0s - loss: 3.1779 - accuracy: 0.1818 - 25ms/epoch - 13ms/step


#######################################################


the model mod40 use a learning rate = 7, l2 regularization = 1 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.38636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.38636

Epoch 3: val_accuracy did not improve from 0.38636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.38636

Epoch 5: val_accuracy did not improve from 0.38636

Epoch 6: val_accuracy did not improve from 0.38636

Epoch 7: val_accuracy did not improve from 0.38636

Epoch 8: val_accuracy did not improve from 0.38636

Epoch 9: val_accuracy did not improve from 0.38636

Epoch 10: val_accuracy did not improve from 0.38636

Epoch 11: val_accuracy did not improve from 0.38636

Epoch 12: val_accuracy did not improve from 0.38636

Epoch 13: val_accuracy did not improve from 0.38636

Epoch 14: val_accuracy did not improve from 0.38636

Epoch 15: val_accuracy did not improve from 0.38636

Epoch 16: val_accuracy did not improve from 0.38636

Epoch 17: val_accuracy did not improve from 0.38636

Epoch 18: val_accuracy did not improve from 0.38636

Epoch 19: val_accuracy did not improve from 0.38636

Epoch 20: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5

Epoch 21: val_accuracy did not improve from 0.40909

Epoch 22: val_accuracy did not improve from 0.40909

Epoch 23: val_accuracy did not improve from 0.40909

Epoch 24: val_accuracy did not improve from 0.40909

Epoch 25: val_accuracy did not improve from 0.40909

Epoch 26: val_accuracy did not improve from 0.40909

Epoch 27: val_accuracy did not improve from 0.40909

Epoch 28: val_accuracy did not improve from 0.40909

Epoch 29: val_accuracy did not improve from 0.40909

Epoch 30: val_accuracy did not improve from 0.40909

Epoch 31: val_accuracy did not improve from 0.40909

Epoch 32: val_accuracy did not improve from 0.40909

Epoch 33: val_accuracy did not improve from 0.40909

Epoch 34: val_accuracy did not improve from 0.40909

Epoch 35: val_accuracy did not improve from 0.40909

Epoch 36: val_accuracy did not improve from 0.40909

Epoch 37: val_accuracy did not improve from 0.40909

Epoch 38: val_accuracy did not improve from 0.40909

Epoch 39: val_accuracy did not improve from 0.40909

Epoch 40: val_accuracy did not improve from 0.40909
2/2 - 0s - loss: 3.4290 - accuracy: 0.4091 - 38ms/epoch - 19ms/step


#######################################################


the model mod41 use a learning rate = 8, l2 regularization = 1 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.52273, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.52273

Epoch 3: val_accuracy did not improve from 0.52273
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.52273

Epoch 5: val_accuracy did not improve from 0.52273

Epoch 6: val_accuracy did not improve from 0.52273

Epoch 7: val_accuracy did not improve from 0.52273

Epoch 8: val_accuracy did not improve from 0.52273

Epoch 9: val_accuracy did not improve from 0.52273

Epoch 10: val_accuracy did not improve from 0.52273

Epoch 11: val_accuracy did not improve from 0.52273

Epoch 12: val_accuracy did not improve from 0.52273

Epoch 13: val_accuracy did not improve from 0.52273

Epoch 14: val_accuracy did not improve from 0.52273

Epoch 15: val_accuracy did not improve from 0.52273

Epoch 16: val_accuracy did not improve from 0.52273

Epoch 17: val_accuracy did not improve from 0.52273

Epoch 18: val_accuracy did not improve from 0.52273

Epoch 19: val_accuracy did not improve from 0.52273

Epoch 20: val_accuracy did not improve from 0.52273

Epoch 21: val_accuracy did not improve from 0.52273
2/2 - 0s - loss: 3.7408 - accuracy: 0.5227 - 25ms/epoch - 12ms/step


#######################################################


the model mod42 use a learning rate = 9, l2 regularization = 1 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.22727, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.22727

Epoch 3: val_accuracy did not improve from 0.22727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.22727

Epoch 5: val_accuracy did not improve from 0.22727

Epoch 6: val_accuracy did not improve from 0.22727

Epoch 7: val_accuracy did not improve from 0.22727

Epoch 8: val_accuracy did not improve from 0.22727

Epoch 9: val_accuracy did not improve from 0.22727

Epoch 10: val_accuracy did not improve from 0.22727

Epoch 11: val_accuracy did not improve from 0.22727

Epoch 12: val_accuracy did not improve from 0.22727

Epoch 13: val_accuracy did not improve from 0.22727

Epoch 14: val_accuracy did not improve from 0.22727

Epoch 15: val_accuracy did not improve from 0.22727

Epoch 16: val_accuracy did not improve from 0.22727

Epoch 17: val_accuracy did not improve from 0.22727

Epoch 18: val_accuracy did not improve from 0.22727

Epoch 19: val_accuracy did not improve from 0.22727

Epoch 20: val_accuracy did not improve from 0.22727

Epoch 21: val_accuracy did not improve from 0.22727
2/2 - 0s - loss: 3.8253 - accuracy: 0.2273 - 24ms/epoch - 12ms/step


#######################################################


the model mod43 use a learning rate = 0, l2 regularization = 1 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.86364

Epoch 5: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4630 - accuracy: 0.8864 - 24ms/epoch - 12ms/step


#######################################################


the model mod44 use a learning rate = 1, l2 regularization = 1 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.75000 to 0.84091, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3539 - accuracy: 0.8864 - 43ms/epoch - 21ms/step


#######################################################


the model mod45 use a learning rate = 2, l2 regularization = 1 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.70455

Epoch 3: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.70455

Epoch 5: val_accuracy did not improve from 0.70455

Epoch 6: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.79545

Epoch 10: val_accuracy did not improve from 0.79545

Epoch 11: val_accuracy did not improve from 0.79545

Epoch 12: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.81818

Epoch 14: val_accuracy did not improve from 0.81818

Epoch 15: val_accuracy did not improve from 0.81818

Epoch 16: val_accuracy did not improve from 0.81818

Epoch 17: val_accuracy did not improve from 0.81818

Epoch 18: val_accuracy did not improve from 0.81818

Epoch 19: val_accuracy did not improve from 0.81818

Epoch 20: val_accuracy did not improve from 0.81818

Epoch 21: val_accuracy did not improve from 0.81818

Epoch 22: val_accuracy did not improve from 0.81818

Epoch 23: val_accuracy did not improve from 0.81818

Epoch 24: val_accuracy did not improve from 0.81818

Epoch 25: val_accuracy did not improve from 0.81818

Epoch 26: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 27: val_accuracy did not improve from 0.84091

Epoch 28: val_accuracy did not improve from 0.84091

Epoch 29: val_accuracy did not improve from 0.84091

Epoch 30: val_accuracy did not improve from 0.84091

Epoch 31: val_accuracy did not improve from 0.84091

Epoch 32: val_accuracy did not improve from 0.84091

Epoch 33: val_accuracy did not improve from 0.84091

Epoch 34: val_accuracy did not improve from 0.84091

Epoch 35: val_accuracy did not improve from 0.84091

Epoch 36: val_accuracy did not improve from 0.84091

Epoch 37: val_accuracy did not improve from 0.84091

Epoch 38: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 39: val_accuracy did not improve from 0.86364

Epoch 40: val_accuracy did not improve from 0.86364

Epoch 41: val_accuracy did not improve from 0.86364

Epoch 42: val_accuracy did not improve from 0.86364

Epoch 43: val_accuracy did not improve from 0.86364

Epoch 44: val_accuracy did not improve from 0.86364

Epoch 45: val_accuracy did not improve from 0.86364

Epoch 46: val_accuracy did not improve from 0.86364

Epoch 47: val_accuracy did not improve from 0.86364

Epoch 48: val_accuracy did not improve from 0.86364

Epoch 49: val_accuracy did not improve from 0.86364

Epoch 50: val_accuracy did not improve from 0.86364

Epoch 51: val_accuracy did not improve from 0.86364

Epoch 52: val_accuracy did not improve from 0.86364

Epoch 53: val_accuracy did not improve from 0.86364

Epoch 54: val_accuracy did not improve from 0.86364

Epoch 55: val_accuracy did not improve from 0.86364

Epoch 56: val_accuracy did not improve from 0.86364

Epoch 57: val_accuracy did not improve from 0.86364

Epoch 58: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 1.0730 - accuracy: 0.8636 - 25ms/epoch - 13ms/step


#######################################################


the model mod46 use a learning rate = 3, l2 regularization = 1 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.54545

Epoch 3: val_accuracy did not improve from 0.54545

Epoch 4: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.54545

Epoch 6: val_accuracy did not improve from 0.54545

Epoch 7: val_accuracy did not improve from 0.54545

Epoch 8: val_accuracy did not improve from 0.54545

Epoch 9: val_accuracy did not improve from 0.54545

Epoch 10: val_accuracy did not improve from 0.54545

Epoch 11: val_accuracy did not improve from 0.54545

Epoch 12: val_accuracy did not improve from 0.54545

Epoch 13: val_accuracy did not improve from 0.54545

Epoch 14: val_accuracy did not improve from 0.54545

Epoch 15: val_accuracy did not improve from 0.54545

Epoch 16: val_accuracy did not improve from 0.54545

Epoch 17: val_accuracy did not improve from 0.54545

Epoch 18: val_accuracy did not improve from 0.54545

Epoch 19: val_accuracy did not improve from 0.54545

Epoch 20: val_accuracy did not improve from 0.54545

Epoch 21: val_accuracy did not improve from 0.54545
2/2 - 0s - loss: 3.3518 - accuracy: 0.5455 - 23ms/epoch - 12ms/step


#######################################################


the model mod47 use a learning rate = 4, l2 regularization = 1 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.36364 to 0.38636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5

Epoch 4: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.43182

Epoch 6: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.50000

Epoch 10: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5

Epoch 11: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.56818

Epoch 13: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 14: val_accuracy did not improve from 0.59091

Epoch 15: val_accuracy did not improve from 0.59091

Epoch 16: val_accuracy did not improve from 0.59091

Epoch 17: val_accuracy did not improve from 0.59091

Epoch 18: val_accuracy did not improve from 0.59091

Epoch 19: val_accuracy did not improve from 0.59091

Epoch 20: val_accuracy did not improve from 0.59091

Epoch 21: val_accuracy improved from 0.59091 to 0.63636, saving model to best_model.h5

Epoch 22: val_accuracy did not improve from 0.63636

Epoch 23: val_accuracy did not improve from 0.63636

Epoch 24: val_accuracy did not improve from 0.63636

Epoch 25: val_accuracy did not improve from 0.63636

Epoch 26: val_accuracy did not improve from 0.63636

Epoch 27: val_accuracy did not improve from 0.63636

Epoch 28: val_accuracy did not improve from 0.63636

Epoch 29: val_accuracy did not improve from 0.63636

Epoch 30: val_accuracy did not improve from 0.63636

Epoch 31: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 32: val_accuracy did not improve from 0.65909

Epoch 33: val_accuracy did not improve from 0.65909

Epoch 34: val_accuracy did not improve from 0.65909

Epoch 35: val_accuracy did not improve from 0.65909

Epoch 36: val_accuracy did not improve from 0.65909

Epoch 37: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5

Epoch 38: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 39: val_accuracy did not improve from 0.70455

Epoch 40: val_accuracy did not improve from 0.70455

Epoch 41: val_accuracy did not improve from 0.70455

Epoch 42: val_accuracy did not improve from 0.70455

Epoch 43: val_accuracy did not improve from 0.70455

Epoch 44: val_accuracy did not improve from 0.70455

Epoch 45: val_accuracy did not improve from 0.70455

Epoch 46: val_accuracy did not improve from 0.70455

Epoch 47: val_accuracy did not improve from 0.70455

Epoch 48: val_accuracy did not improve from 0.70455

Epoch 49: val_accuracy did not improve from 0.70455

Epoch 50: val_accuracy did not improve from 0.70455

Epoch 51: val_accuracy did not improve from 0.70455

Epoch 52: val_accuracy did not improve from 0.70455

Epoch 53: val_accuracy did not improve from 0.70455

Epoch 54: val_accuracy did not improve from 0.70455

Epoch 55: val_accuracy did not improve from 0.70455

Epoch 56: val_accuracy did not improve from 0.70455

Epoch 57: val_accuracy did not improve from 0.70455

Epoch 58: val_accuracy did not improve from 0.70455
2/2 - 0s - loss: 1.8862 - accuracy: 0.7045 - 24ms/epoch - 12ms/step


#######################################################


the model mod48 use a learning rate = 5, l2 regularization = 1 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.40909, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.40909

Epoch 3: val_accuracy did not improve from 0.40909

Epoch 4: val_accuracy did not improve from 0.40909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.40909

Epoch 6: val_accuracy did not improve from 0.40909

Epoch 7: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.43182

Epoch 9: val_accuracy did not improve from 0.43182

Epoch 10: val_accuracy did not improve from 0.43182

Epoch 11: val_accuracy did not improve from 0.43182

Epoch 12: val_accuracy did not improve from 0.43182

Epoch 13: val_accuracy did not improve from 0.43182

Epoch 14: val_accuracy did not improve from 0.43182

Epoch 15: val_accuracy did not improve from 0.43182

Epoch 16: val_accuracy did not improve from 0.43182

Epoch 17: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.45455

Epoch 19: val_accuracy did not improve from 0.45455

Epoch 20: val_accuracy did not improve from 0.45455

Epoch 21: val_accuracy did not improve from 0.45455

Epoch 22: val_accuracy did not improve from 0.45455

Epoch 23: val_accuracy did not improve from 0.45455

Epoch 24: val_accuracy did not improve from 0.45455

Epoch 25: val_accuracy did not improve from 0.45455

Epoch 26: val_accuracy did not improve from 0.45455

Epoch 27: val_accuracy did not improve from 0.45455

Epoch 28: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5

Epoch 29: val_accuracy did not improve from 0.47727

Epoch 30: val_accuracy did not improve from 0.47727

Epoch 31: val_accuracy did not improve from 0.47727

Epoch 32: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5

Epoch 33: val_accuracy did not improve from 0.50000

Epoch 34: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5

Epoch 35: val_accuracy did not improve from 0.52273

Epoch 36: val_accuracy did not improve from 0.52273

Epoch 37: val_accuracy did not improve from 0.52273

Epoch 38: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5

Epoch 39: val_accuracy did not improve from 0.56818

Epoch 40: val_accuracy did not improve from 0.56818

Epoch 41: val_accuracy did not improve from 0.56818

Epoch 42: val_accuracy did not improve from 0.56818

Epoch 43: val_accuracy did not improve from 0.56818

Epoch 44: val_accuracy did not improve from 0.56818

Epoch 45: val_accuracy did not improve from 0.56818

Epoch 46: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 47: val_accuracy did not improve from 0.59091

Epoch 48: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 49: val_accuracy did not improve from 0.61364

Epoch 50: val_accuracy did not improve from 0.61364

Epoch 51: val_accuracy did not improve from 0.61364

Epoch 52: val_accuracy did not improve from 0.61364

Epoch 53: val_accuracy did not improve from 0.61364

Epoch 54: val_accuracy did not improve from 0.61364

Epoch 55: val_accuracy did not improve from 0.61364

Epoch 56: val_accuracy did not improve from 0.61364

Epoch 57: val_accuracy did not improve from 0.61364

Epoch 58: val_accuracy did not improve from 0.61364

Epoch 59: val_accuracy did not improve from 0.61364

Epoch 60: val_accuracy did not improve from 0.61364

Epoch 61: val_accuracy did not improve from 0.61364

Epoch 62: val_accuracy did not improve from 0.61364

Epoch 63: val_accuracy did not improve from 0.61364

Epoch 64: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5

Epoch 65: val_accuracy did not improve from 0.63636

Epoch 66: val_accuracy did not improve from 0.63636

Epoch 67: val_accuracy did not improve from 0.63636

Epoch 68: val_accuracy did not improve from 0.63636

Epoch 69: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 70: val_accuracy did not improve from 0.65909

Epoch 71: val_accuracy did not improve from 0.65909

Epoch 72: val_accuracy did not improve from 0.65909

Epoch 73: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5

Epoch 74: val_accuracy did not improve from 0.68182

Epoch 75: val_accuracy did not improve from 0.68182

Epoch 76: val_accuracy did not improve from 0.68182

Epoch 77: val_accuracy did not improve from 0.68182

Epoch 78: val_accuracy did not improve from 0.68182

Epoch 79: val_accuracy did not improve from 0.68182

Epoch 80: val_accuracy did not improve from 0.68182

Epoch 81: val_accuracy did not improve from 0.68182

Epoch 82: val_accuracy did not improve from 0.68182

Epoch 83: val_accuracy did not improve from 0.68182

Epoch 84: val_accuracy did not improve from 0.68182

Epoch 85: val_accuracy did not improve from 0.68182

Epoch 86: val_accuracy did not improve from 0.68182

Epoch 87: val_accuracy did not improve from 0.68182

Epoch 88: val_accuracy did not improve from 0.68182

Epoch 89: val_accuracy did not improve from 0.68182

Epoch 90: val_accuracy did not improve from 0.68182

Epoch 91: val_accuracy did not improve from 0.68182

Epoch 92: val_accuracy did not improve from 0.68182

Epoch 93: val_accuracy did not improve from 0.68182
2/2 - 0s - loss: 2.2558 - accuracy: 0.6818 - 24ms/epoch - 12ms/step


#######################################################


the model mod49 use a learning rate = 6, l2 regularization = 1 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.61364, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.61364

Epoch 3: val_accuracy did not improve from 0.61364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.61364

Epoch 5: val_accuracy did not improve from 0.61364

Epoch 6: val_accuracy did not improve from 0.61364

Epoch 7: val_accuracy did not improve from 0.61364

Epoch 8: val_accuracy did not improve from 0.61364

Epoch 9: val_accuracy did not improve from 0.61364

Epoch 10: val_accuracy did not improve from 0.61364

Epoch 11: val_accuracy did not improve from 0.61364

Epoch 12: val_accuracy did not improve from 0.61364

Epoch 13: val_accuracy did not improve from 0.61364

Epoch 14: val_accuracy did not improve from 0.61364

Epoch 15: val_accuracy did not improve from 0.61364

Epoch 16: val_accuracy did not improve from 0.61364

Epoch 17: val_accuracy did not improve from 0.61364

Epoch 18: val_accuracy did not improve from 0.61364

Epoch 19: val_accuracy did not improve from 0.61364

Epoch 20: val_accuracy did not improve from 0.61364

Epoch 21: val_accuracy did not improve from 0.61364
2/2 - 0s - loss: 3.1395 - accuracy: 0.6136 - 24ms/epoch - 12ms/step


#######################################################


the model mod50 use a learning rate = 7, l2 regularization = 1 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.31818, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.31818

Epoch 3: val_accuracy did not improve from 0.31818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.31818

Epoch 5: val_accuracy did not improve from 0.31818

Epoch 6: val_accuracy did not improve from 0.31818

Epoch 7: val_accuracy did not improve from 0.31818

Epoch 8: val_accuracy did not improve from 0.31818

Epoch 9: val_accuracy did not improve from 0.31818

Epoch 10: val_accuracy did not improve from 0.31818

Epoch 11: val_accuracy did not improve from 0.31818

Epoch 12: val_accuracy did not improve from 0.31818

Epoch 13: val_accuracy did not improve from 0.31818

Epoch 14: val_accuracy did not improve from 0.31818

Epoch 15: val_accuracy did not improve from 0.31818

Epoch 16: val_accuracy did not improve from 0.31818

Epoch 17: val_accuracy did not improve from 0.31818

Epoch 18: val_accuracy did not improve from 0.31818

Epoch 19: val_accuracy did not improve from 0.31818

Epoch 20: val_accuracy did not improve from 0.31818

Epoch 21: val_accuracy did not improve from 0.31818
2/2 - 0s - loss: 3.5574 - accuracy: 0.3182 - 48ms/epoch - 24ms/step


#######################################################


the model mod51 use a learning rate = 8, l2 regularization = 1 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.75000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.75000

Epoch 4: val_accuracy did not improve from 0.75000

Epoch 5: val_accuracy did not improve from 0.75000

Epoch 6: val_accuracy did not improve from 0.75000

Epoch 7: val_accuracy did not improve from 0.75000

Epoch 8: val_accuracy did not improve from 0.75000

Epoch 9: val_accuracy did not improve from 0.75000

Epoch 10: val_accuracy did not improve from 0.75000

Epoch 11: val_accuracy did not improve from 0.75000

Epoch 12: val_accuracy did not improve from 0.75000

Epoch 13: val_accuracy did not improve from 0.75000

Epoch 14: val_accuracy did not improve from 0.75000

Epoch 15: val_accuracy did not improve from 0.75000

Epoch 16: val_accuracy did not improve from 0.75000

Epoch 17: val_accuracy did not improve from 0.75000

Epoch 18: val_accuracy did not improve from 0.75000

Epoch 19: val_accuracy did not improve from 0.75000

Epoch 20: val_accuracy did not improve from 0.75000

Epoch 21: val_accuracy did not improve from 0.75000
2/2 - 0s - loss: 3.0881 - accuracy: 0.7500 - 23ms/epoch - 12ms/step


#######################################################


the model mod52 use a learning rate = 9, l2 regularization = 1 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.50000

Epoch 3: val_accuracy did not improve from 0.50000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.50000

Epoch 5: val_accuracy did not improve from 0.50000

Epoch 6: val_accuracy did not improve from 0.50000

Epoch 7: val_accuracy did not improve from 0.50000

Epoch 8: val_accuracy did not improve from 0.50000

Epoch 9: val_accuracy did not improve from 0.50000

Epoch 10: val_accuracy did not improve from 0.50000

Epoch 11: val_accuracy did not improve from 0.50000

Epoch 12: val_accuracy did not improve from 0.50000

Epoch 13: val_accuracy did not improve from 0.50000

Epoch 14: val_accuracy did not improve from 0.50000

Epoch 15: val_accuracy did not improve from 0.50000

Epoch 16: val_accuracy did not improve from 0.50000

Epoch 17: val_accuracy did not improve from 0.50000

Epoch 18: val_accuracy did not improve from 0.50000

Epoch 19: val_accuracy did not improve from 0.50000

Epoch 20: val_accuracy did not improve from 0.50000

Epoch 21: val_accuracy did not improve from 0.50000
2/2 - 0s - loss: 3.8460 - accuracy: 0.5000 - 37ms/epoch - 18ms/step


#######################################################


the model mod53 use a learning rate = 0, l2 regularization = 1 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.75000 to 0.88636, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4138 - accuracy: 0.8636 - 24ms/epoch - 12ms/step


#######################################################


the model mod54 use a learning rate = 1, l2 regularization = 1 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3810 - accuracy: 0.8182 - 25ms/epoch - 12ms/step


#######################################################


the model mod55 use a learning rate = 2, l2 regularization = 1 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.72727 to 0.86364, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.93182

Epoch 19: val_accuracy did not improve from 0.93182

Epoch 20: val_accuracy did not improve from 0.93182

Epoch 21: val_accuracy did not improve from 0.93182

Epoch 22: val_accuracy did not improve from 0.93182

Epoch 23: val_accuracy did not improve from 0.93182

Epoch 24: val_accuracy did not improve from 0.93182

Epoch 25: val_accuracy did not improve from 0.93182

Epoch 26: val_accuracy did not improve from 0.93182

Epoch 27: val_accuracy did not improve from 0.93182

Epoch 28: val_accuracy did not improve from 0.93182

Epoch 29: val_accuracy did not improve from 0.93182

Epoch 30: val_accuracy did not improve from 0.93182

Epoch 31: val_accuracy did not improve from 0.93182

Epoch 32: val_accuracy did not improve from 0.93182

Epoch 33: val_accuracy did not improve from 0.93182

Epoch 34: val_accuracy did not improve from 0.93182

Epoch 35: val_accuracy did not improve from 0.93182

Epoch 36: val_accuracy did not improve from 0.93182

Epoch 37: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.3533 - accuracy: 0.8864 - 24ms/epoch - 12ms/step


#######################################################


the model mod56 use a learning rate = 3, l2 regularization = 1 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.75000 to 0.90909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.90909

Epoch 4: val_accuracy did not improve from 0.90909

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3430 - accuracy: 0.8864 - 42ms/epoch - 21ms/step


#######################################################


the model mod57 use a learning rate = 4, l2 regularization = 1 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3799 - accuracy: 0.8409 - 24ms/epoch - 12ms/step


#######################################################


the model mod58 use a learning rate = 5, l2 regularization = 1 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.93182

Epoch 12: val_accuracy did not improve from 0.93182

Epoch 13: val_accuracy did not improve from 0.93182

Epoch 14: val_accuracy did not improve from 0.93182

Epoch 15: val_accuracy did not improve from 0.93182

Epoch 16: val_accuracy did not improve from 0.93182

Epoch 17: val_accuracy did not improve from 0.93182

Epoch 18: val_accuracy did not improve from 0.93182

Epoch 19: val_accuracy did not improve from 0.93182

Epoch 20: val_accuracy did not improve from 0.93182

Epoch 21: val_accuracy did not improve from 0.93182

Epoch 22: val_accuracy did not improve from 0.93182

Epoch 23: val_accuracy did not improve from 0.93182

Epoch 24: val_accuracy did not improve from 0.93182

Epoch 25: val_accuracy did not improve from 0.93182

Epoch 26: val_accuracy did not improve from 0.93182

Epoch 27: val_accuracy did not improve from 0.93182

Epoch 28: val_accuracy did not improve from 0.93182

Epoch 29: val_accuracy did not improve from 0.93182

Epoch 30: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.4089 - accuracy: 0.8182 - 38ms/epoch - 19ms/step


#######################################################


the model mod59 use a learning rate = 6, l2 regularization = 1 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.70455 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.88636

Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy improved from 0.90909 to 0.95455, saving model to best_model.h5

Epoch 22: val_accuracy did not improve from 0.95455

Epoch 23: val_accuracy did not improve from 0.95455

Epoch 24: val_accuracy did not improve from 0.95455

Epoch 25: val_accuracy did not improve from 0.95455

Epoch 26: val_accuracy did not improve from 0.95455

Epoch 27: val_accuracy did not improve from 0.95455

Epoch 28: val_accuracy did not improve from 0.95455

Epoch 29: val_accuracy did not improve from 0.95455

Epoch 30: val_accuracy did not improve from 0.95455

Epoch 31: val_accuracy did not improve from 0.95455

Epoch 32: val_accuracy did not improve from 0.95455

Epoch 33: val_accuracy did not improve from 0.95455

Epoch 34: val_accuracy did not improve from 0.95455

Epoch 35: val_accuracy did not improve from 0.95455

Epoch 36: val_accuracy did not improve from 0.95455

Epoch 37: val_accuracy did not improve from 0.95455

Epoch 38: val_accuracy did not improve from 0.95455

Epoch 39: val_accuracy did not improve from 0.95455

Epoch 40: val_accuracy did not improve from 0.95455

Epoch 41: val_accuracy did not improve from 0.95455
2/2 - 0s - loss: 0.4011 - accuracy: 0.8182 - 34ms/epoch - 17ms/step


#######################################################


the model mod60 use a learning rate = 7, l2 regularization = 1 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.90909, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.90909

Epoch 3: val_accuracy did not improve from 0.90909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3912 - accuracy: 0.9091 - 28ms/epoch - 14ms/step


#######################################################


the model mod61 use a learning rate = 8, l2 regularization = 1 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.93182

Epoch 12: val_accuracy did not improve from 0.93182

Epoch 13: val_accuracy did not improve from 0.93182

Epoch 14: val_accuracy did not improve from 0.93182

Epoch 15: val_accuracy did not improve from 0.93182

Epoch 16: val_accuracy did not improve from 0.93182

Epoch 17: val_accuracy did not improve from 0.93182

Epoch 18: val_accuracy did not improve from 0.93182

Epoch 19: val_accuracy did not improve from 0.93182

Epoch 20: val_accuracy did not improve from 0.93182

Epoch 21: val_accuracy did not improve from 0.93182

Epoch 22: val_accuracy did not improve from 0.93182

Epoch 23: val_accuracy did not improve from 0.93182

Epoch 24: val_accuracy did not improve from 0.93182

Epoch 25: val_accuracy did not improve from 0.93182

Epoch 26: val_accuracy did not improve from 0.93182

Epoch 27: val_accuracy did not improve from 0.93182

Epoch 28: val_accuracy did not improve from 0.93182

Epoch 29: val_accuracy did not improve from 0.93182

Epoch 30: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.4415 - accuracy: 0.8182 - 42ms/epoch - 21ms/step


#######################################################


the model mod62 use a learning rate = 9, l2 regularization = 1 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 4: val_accuracy improved from 0.88636 to 0.93182, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.93182

Epoch 6: val_accuracy did not improve from 0.93182

Epoch 7: val_accuracy did not improve from 0.93182

Epoch 8: val_accuracy did not improve from 0.93182

Epoch 9: val_accuracy did not improve from 0.93182

Epoch 10: val_accuracy did not improve from 0.93182

Epoch 11: val_accuracy did not improve from 0.93182

Epoch 12: val_accuracy did not improve from 0.93182

Epoch 13: val_accuracy did not improve from 0.93182

Epoch 14: val_accuracy did not improve from 0.93182

Epoch 15: val_accuracy did not improve from 0.93182

Epoch 16: val_accuracy did not improve from 0.93182

Epoch 17: val_accuracy did not improve from 0.93182

Epoch 18: val_accuracy did not improve from 0.93182

Epoch 19: val_accuracy did not improve from 0.93182

Epoch 20: val_accuracy did not improve from 0.93182

Epoch 21: val_accuracy did not improve from 0.93182

Epoch 22: val_accuracy did not improve from 0.93182

Epoch 23: val_accuracy did not improve from 0.93182

Epoch 24: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.4065 - accuracy: 0.8409 - 24ms/epoch - 12ms/step


#######################################################


the model mod63 use a learning rate = 0, l2 regularization = 2 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.81818

Epoch 3: val_accuracy did not improve from 0.81818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.81818

Epoch 5: val_accuracy did not improve from 0.81818

Epoch 6: val_accuracy did not improve from 0.81818

Epoch 7: val_accuracy did not improve from 0.81818

Epoch 8: val_accuracy did not improve from 0.81818

Epoch 9: val_accuracy did not improve from 0.81818

Epoch 10: val_accuracy did not improve from 0.81818

Epoch 11: val_accuracy did not improve from 0.81818

Epoch 12: val_accuracy did not improve from 0.81818

Epoch 13: val_accuracy did not improve from 0.81818

Epoch 14: val_accuracy did not improve from 0.81818

Epoch 15: val_accuracy did not improve from 0.81818

Epoch 16: val_accuracy did not improve from 0.81818

Epoch 17: val_accuracy did not improve from 0.81818

Epoch 18: val_accuracy did not improve from 0.81818

Epoch 19: val_accuracy did not improve from 0.81818

Epoch 20: val_accuracy did not improve from 0.81818

Epoch 21: val_accuracy did not improve from 0.81818
2/2 - 0s - loss: 0.6966 - accuracy: 0.5455 - 26ms/epoch - 13ms/step


#######################################################


the model mod64 use a learning rate = 1, l2 regularization = 2 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.90909, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.90909

Epoch 3: val_accuracy did not improve from 0.90909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.7001 - accuracy: 0.8182 - 27ms/epoch - 14ms/step


#######################################################


the model mod65 use a learning rate = 2, l2 regularization = 2 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.84091

Epoch 4: val_accuracy did not improve from 0.84091

Epoch 5: val_accuracy did not improve from 0.84091

Epoch 6: val_accuracy did not improve from 0.84091

Epoch 7: val_accuracy did not improve from 0.84091

Epoch 8: val_accuracy did not improve from 0.84091

Epoch 9: val_accuracy did not improve from 0.84091

Epoch 10: val_accuracy did not improve from 0.84091

Epoch 11: val_accuracy did not improve from 0.84091

Epoch 12: val_accuracy did not improve from 0.84091

Epoch 13: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy did not improve from 0.88636

Epoch 28: val_accuracy did not improve from 0.88636

Epoch 29: val_accuracy did not improve from 0.88636

Epoch 30: val_accuracy did not improve from 0.88636

Epoch 31: val_accuracy did not improve from 0.88636

Epoch 32: val_accuracy did not improve from 0.88636

Epoch 33: val_accuracy did not improve from 0.88636

Epoch 34: val_accuracy did not improve from 0.88636

Epoch 35: val_accuracy did not improve from 0.88636

Epoch 36: val_accuracy did not improve from 0.88636

Epoch 37: val_accuracy did not improve from 0.88636

Epoch 38: val_accuracy did not improve from 0.88636

Epoch 39: val_accuracy did not improve from 0.88636

Epoch 40: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4157 - accuracy: 0.8636 - 25ms/epoch - 12ms/step


#######################################################


the model mod66 use a learning rate = 3, l2 regularization = 2 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.43182, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.43182

Epoch 3: val_accuracy did not improve from 0.43182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.43182

Epoch 5: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.45455 to 0.52273, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.52273

Epoch 8: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5

Epoch 9: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 10: val_accuracy improved from 0.59091 to 0.63636, saving model to best_model.h5

Epoch 11: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 12: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5

Epoch 13: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 14: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 15: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.75000

Epoch 17: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.77273

Epoch 19: val_accuracy did not improve from 0.77273

Epoch 20: val_accuracy did not improve from 0.77273

Epoch 21: val_accuracy did not improve from 0.77273

Epoch 22: val_accuracy did not improve from 0.77273

Epoch 23: val_accuracy did not improve from 0.77273

Epoch 24: val_accuracy did not improve from 0.77273

Epoch 25: val_accuracy did not improve from 0.77273

Epoch 26: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 27: val_accuracy did not improve from 0.79545

Epoch 28: val_accuracy did not improve from 0.79545

Epoch 29: val_accuracy did not improve from 0.79545

Epoch 30: val_accuracy did not improve from 0.79545

Epoch 31: val_accuracy did not improve from 0.79545

Epoch 32: val_accuracy did not improve from 0.79545

Epoch 33: val_accuracy did not improve from 0.79545

Epoch 34: val_accuracy did not improve from 0.79545

Epoch 35: val_accuracy did not improve from 0.79545

Epoch 36: val_accuracy did not improve from 0.79545

Epoch 37: val_accuracy did not improve from 0.79545

Epoch 38: val_accuracy did not improve from 0.79545

Epoch 39: val_accuracy did not improve from 0.79545

Epoch 40: val_accuracy did not improve from 0.79545

Epoch 41: val_accuracy did not improve from 0.79545

Epoch 42: val_accuracy did not improve from 0.79545

Epoch 43: val_accuracy did not improve from 0.79545

Epoch 44: val_accuracy did not improve from 0.79545

Epoch 45: val_accuracy did not improve from 0.79545

Epoch 46: val_accuracy did not improve from 0.79545
2/2 - 0s - loss: 0.6703 - accuracy: 0.7955 - 26ms/epoch - 13ms/step


#######################################################


the model mod67 use a learning rate = 4, l2 regularization = 2 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.70455 to 0.75000, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.75000 to 0.79545, saving model to best_model.h5

Epoch 4: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.81818

Epoch 6: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3950 - accuracy: 0.8864 - 29ms/epoch - 15ms/step


#######################################################


the model mod68 use a learning rate = 5, l2 regularization = 2 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.56818 to 0.61364, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.61364 to 0.65909, saving model to best_model.h5

Epoch 4: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.72727 to 0.81818, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.84091

Epoch 9: val_accuracy did not improve from 0.84091

Epoch 10: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909

Epoch 35: val_accuracy did not improve from 0.90909

Epoch 36: val_accuracy did not improve from 0.90909

Epoch 37: val_accuracy did not improve from 0.90909

Epoch 38: val_accuracy did not improve from 0.90909

Epoch 39: val_accuracy did not improve from 0.90909

Epoch 40: val_accuracy did not improve from 0.90909

Epoch 41: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3984 - accuracy: 0.8636 - 46ms/epoch - 23ms/step


#######################################################


the model mod69 use a learning rate = 6, l2 regularization = 2 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.63636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.63636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 4: val_accuracy did not improve from 0.65909

Epoch 5: val_accuracy did not improve from 0.65909

Epoch 6: val_accuracy did not improve from 0.65909

Epoch 7: val_accuracy did not improve from 0.65909

Epoch 8: val_accuracy did not improve from 0.65909

Epoch 9: val_accuracy did not improve from 0.65909

Epoch 10: val_accuracy did not improve from 0.65909

Epoch 11: val_accuracy did not improve from 0.65909

Epoch 12: val_accuracy did not improve from 0.65909

Epoch 13: val_accuracy did not improve from 0.65909

Epoch 14: val_accuracy did not improve from 0.65909

Epoch 15: val_accuracy did not improve from 0.65909

Epoch 16: val_accuracy did not improve from 0.65909

Epoch 17: val_accuracy did not improve from 0.65909

Epoch 18: val_accuracy did not improve from 0.65909

Epoch 19: val_accuracy did not improve from 0.65909

Epoch 20: val_accuracy did not improve from 0.65909

Epoch 21: val_accuracy did not improve from 0.65909

Epoch 22: val_accuracy did not improve from 0.65909

Epoch 23: val_accuracy did not improve from 0.65909
2/2 - 0s - loss: 0.9318 - accuracy: 0.6591 - 30ms/epoch - 15ms/step


#######################################################


the model mod70 use a learning rate = 7, l2 regularization = 2 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.75000

Epoch 3: val_accuracy did not improve from 0.75000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.75000

Epoch 5: val_accuracy did not improve from 0.75000

Epoch 6: val_accuracy did not improve from 0.75000

Epoch 7: val_accuracy did not improve from 0.75000

Epoch 8: val_accuracy did not improve from 0.75000

Epoch 9: val_accuracy did not improve from 0.75000

Epoch 10: val_accuracy did not improve from 0.75000

Epoch 11: val_accuracy did not improve from 0.75000

Epoch 12: val_accuracy did not improve from 0.75000

Epoch 13: val_accuracy did not improve from 0.75000

Epoch 14: val_accuracy did not improve from 0.75000

Epoch 15: val_accuracy did not improve from 0.75000

Epoch 16: val_accuracy did not improve from 0.75000

Epoch 17: val_accuracy did not improve from 0.75000

Epoch 18: val_accuracy did not improve from 0.75000

Epoch 19: val_accuracy did not improve from 0.75000

Epoch 20: val_accuracy did not improve from 0.75000

Epoch 21: val_accuracy did not improve from 0.75000
2/2 - 0s - loss: 0.8926 - accuracy: 0.7500 - 34ms/epoch - 17ms/step


#######################################################


the model mod71 use a learning rate = 8, l2 regularization = 2 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.27273, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.27273

Epoch 3: val_accuracy did not improve from 0.27273

Epoch 4: val_accuracy did not improve from 0.27273
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.27273

Epoch 6: val_accuracy did not improve from 0.27273

Epoch 7: val_accuracy did not improve from 0.27273

Epoch 8: val_accuracy did not improve from 0.27273

Epoch 9: val_accuracy did not improve from 0.27273

Epoch 10: val_accuracy did not improve from 0.27273

Epoch 11: val_accuracy did not improve from 0.27273

Epoch 12: val_accuracy did not improve from 0.27273

Epoch 13: val_accuracy did not improve from 0.27273

Epoch 14: val_accuracy did not improve from 0.27273

Epoch 15: val_accuracy did not improve from 0.27273

Epoch 16: val_accuracy did not improve from 0.27273

Epoch 17: val_accuracy did not improve from 0.27273

Epoch 18: val_accuracy did not improve from 0.27273

Epoch 19: val_accuracy did not improve from 0.27273

Epoch 20: val_accuracy did not improve from 0.27273

Epoch 21: val_accuracy did not improve from 0.27273
2/2 - 0s - loss: 1.2854 - accuracy: 0.2727 - 28ms/epoch - 14ms/step


#######################################################


the model mod72 use a learning rate = 9, l2 regularization = 2 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.77273

Epoch 3: val_accuracy did not improve from 0.77273
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.77273

Epoch 5: val_accuracy did not improve from 0.77273

Epoch 6: val_accuracy did not improve from 0.77273

Epoch 7: val_accuracy did not improve from 0.77273

Epoch 8: val_accuracy did not improve from 0.77273

Epoch 9: val_accuracy did not improve from 0.77273

Epoch 10: val_accuracy did not improve from 0.77273

Epoch 11: val_accuracy did not improve from 0.77273

Epoch 12: val_accuracy did not improve from 0.77273

Epoch 13: val_accuracy did not improve from 0.77273

Epoch 14: val_accuracy did not improve from 0.77273

Epoch 15: val_accuracy did not improve from 0.77273

Epoch 16: val_accuracy did not improve from 0.77273

Epoch 17: val_accuracy did not improve from 0.77273

Epoch 18: val_accuracy did not improve from 0.77273

Epoch 19: val_accuracy did not improve from 0.77273

Epoch 20: val_accuracy did not improve from 0.77273

Epoch 21: val_accuracy did not improve from 0.77273
2/2 - 0s - loss: 0.8863 - accuracy: 0.7727 - 51ms/epoch - 26ms/step


#######################################################


the model mod73 use a learning rate = 0, l2 regularization = 2 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.88636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.88636

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4764 - accuracy: 0.7955 - 31ms/epoch - 16ms/step


#######################################################


the model mod74 use a learning rate = 1, l2 regularization = 2 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.59091 to 0.70455, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.72727

Epoch 5: val_accuracy did not improve from 0.72727

Epoch 6: val_accuracy did not improve from 0.72727

Epoch 7: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.77273

Epoch 10: val_accuracy did not improve from 0.77273

Epoch 11: val_accuracy did not improve from 0.77273

Epoch 12: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.79545

Epoch 14: val_accuracy did not improve from 0.79545

Epoch 15: val_accuracy did not improve from 0.79545

Epoch 16: val_accuracy did not improve from 0.79545

Epoch 17: val_accuracy did not improve from 0.79545

Epoch 18: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 19: val_accuracy did not improve from 0.81818

Epoch 20: val_accuracy did not improve from 0.81818

Epoch 21: val_accuracy did not improve from 0.81818

Epoch 22: val_accuracy did not improve from 0.81818

Epoch 23: val_accuracy did not improve from 0.81818

Epoch 24: val_accuracy did not improve from 0.81818

Epoch 25: val_accuracy did not improve from 0.81818

Epoch 26: val_accuracy did not improve from 0.81818

Epoch 27: val_accuracy did not improve from 0.81818

Epoch 28: val_accuracy did not improve from 0.81818

Epoch 29: val_accuracy did not improve from 0.81818

Epoch 30: val_accuracy did not improve from 0.81818

Epoch 31: val_accuracy did not improve from 0.81818

Epoch 32: val_accuracy did not improve from 0.81818

Epoch 33: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 34: val_accuracy did not improve from 0.84091

Epoch 35: val_accuracy did not improve from 0.84091

Epoch 36: val_accuracy did not improve from 0.84091

Epoch 37: val_accuracy did not improve from 0.84091

Epoch 38: val_accuracy did not improve from 0.84091

Epoch 39: val_accuracy did not improve from 0.84091

Epoch 40: val_accuracy did not improve from 0.84091

Epoch 41: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 42: val_accuracy did not improve from 0.86364

Epoch 43: val_accuracy did not improve from 0.86364

Epoch 44: val_accuracy did not improve from 0.86364

Epoch 45: val_accuracy did not improve from 0.86364

Epoch 46: val_accuracy did not improve from 0.86364

Epoch 47: val_accuracy did not improve from 0.86364

Epoch 48: val_accuracy did not improve from 0.86364

Epoch 49: val_accuracy did not improve from 0.86364

Epoch 50: val_accuracy did not improve from 0.86364

Epoch 51: val_accuracy did not improve from 0.86364

Epoch 52: val_accuracy did not improve from 0.86364

Epoch 53: val_accuracy did not improve from 0.86364

Epoch 54: val_accuracy did not improve from 0.86364

Epoch 55: val_accuracy did not improve from 0.86364

Epoch 56: val_accuracy did not improve from 0.86364

Epoch 57: val_accuracy did not improve from 0.86364

Epoch 58: val_accuracy did not improve from 0.86364

Epoch 59: val_accuracy did not improve from 0.86364

Epoch 60: val_accuracy did not improve from 0.86364

Epoch 61: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.4447 - accuracy: 0.8409 - 50ms/epoch - 25ms/step


#######################################################


the model mod75 use a learning rate = 2, l2 regularization = 2 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.52273, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.52273 to 0.59091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.59091

Epoch 4: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.61364

Epoch 6: val_accuracy did not improve from 0.61364

Epoch 7: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.65909

Epoch 10: val_accuracy did not improve from 0.65909

Epoch 11: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.68182

Epoch 13: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 14: val_accuracy did not improve from 0.70455

Epoch 15: val_accuracy improved from 0.70455 to 0.75000, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.75000

Epoch 17: val_accuracy did not improve from 0.75000

Epoch 18: val_accuracy did not improve from 0.75000

Epoch 19: val_accuracy did not improve from 0.75000

Epoch 20: val_accuracy did not improve from 0.75000

Epoch 21: val_accuracy did not improve from 0.75000

Epoch 22: val_accuracy did not improve from 0.75000

Epoch 23: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 24: val_accuracy did not improve from 0.77273

Epoch 25: val_accuracy did not improve from 0.77273

Epoch 26: val_accuracy did not improve from 0.77273

Epoch 27: val_accuracy did not improve from 0.77273

Epoch 28: val_accuracy did not improve from 0.77273

Epoch 29: val_accuracy did not improve from 0.77273

Epoch 30: val_accuracy did not improve from 0.77273

Epoch 31: val_accuracy did not improve from 0.77273

Epoch 32: val_accuracy did not improve from 0.77273

Epoch 33: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 34: val_accuracy did not improve from 0.79545

Epoch 35: val_accuracy did not improve from 0.79545

Epoch 36: val_accuracy did not improve from 0.79545

Epoch 37: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 38: val_accuracy did not improve from 0.81818

Epoch 39: val_accuracy did not improve from 0.81818

Epoch 40: val_accuracy did not improve from 0.81818

Epoch 41: val_accuracy did not improve from 0.81818

Epoch 42: val_accuracy did not improve from 0.81818

Epoch 43: val_accuracy did not improve from 0.81818

Epoch 44: val_accuracy did not improve from 0.81818

Epoch 45: val_accuracy did not improve from 0.81818

Epoch 46: val_accuracy did not improve from 0.81818

Epoch 47: val_accuracy did not improve from 0.81818

Epoch 48: val_accuracy did not improve from 0.81818

Epoch 49: val_accuracy did not improve from 0.81818

Epoch 50: val_accuracy did not improve from 0.81818

Epoch 51: val_accuracy did not improve from 0.81818

Epoch 52: val_accuracy did not improve from 0.81818

Epoch 53: val_accuracy did not improve from 0.81818

Epoch 54: val_accuracy did not improve from 0.81818

Epoch 55: val_accuracy did not improve from 0.81818

Epoch 56: val_accuracy did not improve from 0.81818

Epoch 57: val_accuracy did not improve from 0.81818
2/2 - 0s - loss: 0.6486 - accuracy: 0.8182 - 34ms/epoch - 17ms/step


#######################################################


the model mod76 use a learning rate = 3, l2 regularization = 2 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.59091

Epoch 3: val_accuracy did not improve from 0.59091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.59091

Epoch 5: val_accuracy did not improve from 0.59091

Epoch 6: val_accuracy did not improve from 0.59091

Epoch 7: val_accuracy did not improve from 0.59091

Epoch 8: val_accuracy did not improve from 0.59091

Epoch 9: val_accuracy did not improve from 0.59091

Epoch 10: val_accuracy did not improve from 0.59091

Epoch 11: val_accuracy did not improve from 0.59091

Epoch 12: val_accuracy did not improve from 0.59091

Epoch 13: val_accuracy did not improve from 0.59091

Epoch 14: val_accuracy did not improve from 0.59091

Epoch 15: val_accuracy did not improve from 0.59091

Epoch 16: val_accuracy did not improve from 0.59091

Epoch 17: val_accuracy did not improve from 0.59091

Epoch 18: val_accuracy did not improve from 0.59091

Epoch 19: val_accuracy did not improve from 0.59091

Epoch 20: val_accuracy did not improve from 0.59091

Epoch 21: val_accuracy did not improve from 0.59091
2/2 - 0s - loss: 1.1338 - accuracy: 0.5909 - 36ms/epoch - 18ms/step


#######################################################


the model mod77 use a learning rate = 4, l2 regularization = 2 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.63636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.63636

Epoch 3: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.65909

Epoch 5: val_accuracy did not improve from 0.65909

Epoch 6: val_accuracy did not improve from 0.65909

Epoch 7: val_accuracy improved from 0.65909 to 0.70455, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.70455

Epoch 9: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.72727

Epoch 11: val_accuracy did not improve from 0.72727

Epoch 12: val_accuracy did not improve from 0.72727

Epoch 13: val_accuracy did not improve from 0.72727

Epoch 14: val_accuracy did not improve from 0.72727

Epoch 15: val_accuracy did not improve from 0.72727

Epoch 16: val_accuracy did not improve from 0.72727

Epoch 17: val_accuracy did not improve from 0.72727

Epoch 18: val_accuracy did not improve from 0.72727

Epoch 19: val_accuracy did not improve from 0.72727

Epoch 20: val_accuracy did not improve from 0.72727

Epoch 21: val_accuracy did not improve from 0.72727

Epoch 22: val_accuracy did not improve from 0.72727

Epoch 23: val_accuracy did not improve from 0.72727

Epoch 24: val_accuracy did not improve from 0.72727

Epoch 25: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 26: val_accuracy did not improve from 0.75000

Epoch 27: val_accuracy did not improve from 0.75000

Epoch 28: val_accuracy did not improve from 0.75000

Epoch 29: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 30: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 31: val_accuracy did not improve from 0.79545

Epoch 32: val_accuracy did not improve from 0.79545

Epoch 33: val_accuracy did not improve from 0.79545

Epoch 34: val_accuracy did not improve from 0.79545

Epoch 35: val_accuracy did not improve from 0.79545

Epoch 36: val_accuracy did not improve from 0.79545

Epoch 37: val_accuracy did not improve from 0.79545

Epoch 38: val_accuracy did not improve from 0.79545

Epoch 39: val_accuracy did not improve from 0.79545

Epoch 40: val_accuracy did not improve from 0.79545

Epoch 41: val_accuracy did not improve from 0.79545

Epoch 42: val_accuracy did not improve from 0.79545

Epoch 43: val_accuracy did not improve from 0.79545

Epoch 44: val_accuracy did not improve from 0.79545

Epoch 45: val_accuracy did not improve from 0.79545

Epoch 46: val_accuracy did not improve from 0.79545

Epoch 47: val_accuracy did not improve from 0.79545

Epoch 48: val_accuracy did not improve from 0.79545

Epoch 49: val_accuracy did not improve from 0.79545

Epoch 50: val_accuracy did not improve from 0.79545
2/2 - 0s - loss: 0.7364 - accuracy: 0.7955 - 33ms/epoch - 17ms/step


#######################################################


the model mod78 use a learning rate = 5, l2 regularization = 2 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.72727

Epoch 3: val_accuracy did not improve from 0.72727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.75000

Epoch 6: val_accuracy did not improve from 0.75000

Epoch 7: val_accuracy did not improve from 0.75000

Epoch 8: val_accuracy did not improve from 0.75000

Epoch 9: val_accuracy did not improve from 0.75000

Epoch 10: val_accuracy did not improve from 0.75000

Epoch 11: val_accuracy did not improve from 0.75000

Epoch 12: val_accuracy did not improve from 0.75000

Epoch 13: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 14: val_accuracy did not improve from 0.77273

Epoch 15: val_accuracy did not improve from 0.77273

Epoch 16: val_accuracy did not improve from 0.77273

Epoch 17: val_accuracy did not improve from 0.77273

Epoch 18: val_accuracy did not improve from 0.77273

Epoch 19: val_accuracy did not improve from 0.77273

Epoch 20: val_accuracy did not improve from 0.77273

Epoch 21: val_accuracy did not improve from 0.77273

Epoch 22: val_accuracy did not improve from 0.77273

Epoch 23: val_accuracy did not improve from 0.77273

Epoch 24: val_accuracy did not improve from 0.77273

Epoch 25: val_accuracy did not improve from 0.77273

Epoch 26: val_accuracy did not improve from 0.77273

Epoch 27: val_accuracy did not improve from 0.77273

Epoch 28: val_accuracy did not improve from 0.77273

Epoch 29: val_accuracy did not improve from 0.77273

Epoch 30: val_accuracy did not improve from 0.77273

Epoch 31: val_accuracy did not improve from 0.77273

Epoch 32: val_accuracy did not improve from 0.77273

Epoch 33: val_accuracy did not improve from 0.77273
2/2 - 0s - loss: 0.7695 - accuracy: 0.7727 - 36ms/epoch - 18ms/step


#######################################################


the model mod79 use a learning rate = 6, l2 regularization = 2 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.84091

Epoch 3: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.84091

Epoch 5: val_accuracy did not improve from 0.84091

Epoch 6: val_accuracy did not improve from 0.84091

Epoch 7: val_accuracy did not improve from 0.84091

Epoch 8: val_accuracy did not improve from 0.84091

Epoch 9: val_accuracy did not improve from 0.84091

Epoch 10: val_accuracy did not improve from 0.84091

Epoch 11: val_accuracy did not improve from 0.84091

Epoch 12: val_accuracy did not improve from 0.84091

Epoch 13: val_accuracy did not improve from 0.84091

Epoch 14: val_accuracy did not improve from 0.84091

Epoch 15: val_accuracy did not improve from 0.84091

Epoch 16: val_accuracy did not improve from 0.84091

Epoch 17: val_accuracy did not improve from 0.84091

Epoch 18: val_accuracy did not improve from 0.84091

Epoch 19: val_accuracy did not improve from 0.84091

Epoch 20: val_accuracy did not improve from 0.84091

Epoch 21: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.9676 - accuracy: 0.8409 - 30ms/epoch - 15ms/step


#######################################################


the model mod80 use a learning rate = 7, l2 regularization = 2 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.54545

Epoch 3: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.54545

Epoch 5: val_accuracy did not improve from 0.54545

Epoch 6: val_accuracy did not improve from 0.54545

Epoch 7: val_accuracy did not improve from 0.54545

Epoch 8: val_accuracy did not improve from 0.54545

Epoch 9: val_accuracy did not improve from 0.54545

Epoch 10: val_accuracy did not improve from 0.54545

Epoch 11: val_accuracy did not improve from 0.54545

Epoch 12: val_accuracy did not improve from 0.54545

Epoch 13: val_accuracy did not improve from 0.54545

Epoch 14: val_accuracy did not improve from 0.54545

Epoch 15: val_accuracy did not improve from 0.54545

Epoch 16: val_accuracy did not improve from 0.54545

Epoch 17: val_accuracy did not improve from 0.54545

Epoch 18: val_accuracy did not improve from 0.54545

Epoch 19: val_accuracy did not improve from 0.54545

Epoch 20: val_accuracy did not improve from 0.54545

Epoch 21: val_accuracy did not improve from 0.54545
2/2 - 0s - loss: 0.9429 - accuracy: 0.5455 - 55ms/epoch - 27ms/step


#######################################################


the model mod81 use a learning rate = 8, l2 regularization = 2 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.47727, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.47727

Epoch 3: val_accuracy did not improve from 0.47727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.47727

Epoch 5: val_accuracy did not improve from 0.47727

Epoch 6: val_accuracy did not improve from 0.47727

Epoch 7: val_accuracy did not improve from 0.47727

Epoch 8: val_accuracy did not improve from 0.47727

Epoch 9: val_accuracy did not improve from 0.47727

Epoch 10: val_accuracy did not improve from 0.47727

Epoch 11: val_accuracy did not improve from 0.47727

Epoch 12: val_accuracy did not improve from 0.47727

Epoch 13: val_accuracy did not improve from 0.47727

Epoch 14: val_accuracy did not improve from 0.47727

Epoch 15: val_accuracy did not improve from 0.47727

Epoch 16: val_accuracy did not improve from 0.47727

Epoch 17: val_accuracy did not improve from 0.47727

Epoch 18: val_accuracy did not improve from 0.47727

Epoch 19: val_accuracy did not improve from 0.47727

Epoch 20: val_accuracy did not improve from 0.47727

Epoch 21: val_accuracy did not improve from 0.47727
2/2 - 0s - loss: 1.1341 - accuracy: 0.4773 - 34ms/epoch - 17ms/step


#######################################################


the model mod82 use a learning rate = 9, l2 regularization = 2 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.90909, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.90909

Epoch 3: val_accuracy did not improve from 0.90909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.8586 - accuracy: 0.9091 - 31ms/epoch - 16ms/step


#######################################################


the model mod83 use a learning rate = 0, l2 regularization = 2 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.72727 to 0.86364, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3791 - accuracy: 0.8409 - 35ms/epoch - 17ms/step


#######################################################


the model mod84 use a learning rate = 1, l2 regularization = 2 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.86364, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 4: val_accuracy did not improve from 0.90909

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3972 - accuracy: 0.8864 - 31ms/epoch - 16ms/step


#######################################################


the model mod85 use a learning rate = 2, l2 regularization = 2 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.81818 to 0.90909, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.93182

Epoch 5: val_accuracy did not improve from 0.93182

Epoch 6: val_accuracy did not improve from 0.93182

Epoch 7: val_accuracy did not improve from 0.93182

Epoch 8: val_accuracy did not improve from 0.93182

Epoch 9: val_accuracy did not improve from 0.93182

Epoch 10: val_accuracy did not improve from 0.93182

Epoch 11: val_accuracy did not improve from 0.93182

Epoch 12: val_accuracy did not improve from 0.93182

Epoch 13: val_accuracy did not improve from 0.93182

Epoch 14: val_accuracy did not improve from 0.93182

Epoch 15: val_accuracy did not improve from 0.93182

Epoch 16: val_accuracy did not improve from 0.93182

Epoch 17: val_accuracy did not improve from 0.93182

Epoch 18: val_accuracy did not improve from 0.93182

Epoch 19: val_accuracy did not improve from 0.93182

Epoch 20: val_accuracy did not improve from 0.93182

Epoch 21: val_accuracy did not improve from 0.93182

Epoch 22: val_accuracy did not improve from 0.93182

Epoch 23: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.4254 - accuracy: 0.8409 - 36ms/epoch - 18ms/step


#######################################################


the model mod86 use a learning rate = 3, l2 regularization = 2 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.77273 to 0.81818, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.90909, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4639 - accuracy: 0.8182 - 35ms/epoch - 18ms/step


#######################################################


the model mod87 use a learning rate = 4, l2 regularization = 2 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.79545 to 0.86364, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4546 - accuracy: 0.8864 - 37ms/epoch - 18ms/step


#######################################################


the model mod88 use a learning rate = 5, l2 regularization = 2 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.75000 to 0.81818, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.86364

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364

Epoch 23: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy did not improve from 0.88636

Epoch 28: val_accuracy did not improve from 0.88636

Epoch 29: val_accuracy did not improve from 0.88636

Epoch 30: val_accuracy did not improve from 0.88636

Epoch 31: val_accuracy did not improve from 0.88636

Epoch 32: val_accuracy did not improve from 0.88636

Epoch 33: val_accuracy did not improve from 0.88636

Epoch 34: val_accuracy did not improve from 0.88636

Epoch 35: val_accuracy did not improve from 0.88636

Epoch 36: val_accuracy did not improve from 0.88636

Epoch 37: val_accuracy did not improve from 0.88636

Epoch 38: val_accuracy did not improve from 0.88636

Epoch 39: val_accuracy did not improve from 0.88636

Epoch 40: val_accuracy did not improve from 0.88636

Epoch 41: val_accuracy did not improve from 0.88636

Epoch 42: val_accuracy did not improve from 0.88636

Epoch 43: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5924 - accuracy: 0.8409 - 37ms/epoch - 18ms/step


#######################################################


the model mod89 use a learning rate = 6, l2 regularization = 2 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.81818 to 0.90909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.93182

Epoch 6: val_accuracy did not improve from 0.93182

Epoch 7: val_accuracy did not improve from 0.93182

Epoch 8: val_accuracy did not improve from 0.93182

Epoch 9: val_accuracy did not improve from 0.93182

Epoch 10: val_accuracy did not improve from 0.93182

Epoch 11: val_accuracy did not improve from 0.93182

Epoch 12: val_accuracy did not improve from 0.93182

Epoch 13: val_accuracy did not improve from 0.93182

Epoch 14: val_accuracy did not improve from 0.93182

Epoch 15: val_accuracy did not improve from 0.93182

Epoch 16: val_accuracy did not improve from 0.93182

Epoch 17: val_accuracy did not improve from 0.93182

Epoch 18: val_accuracy did not improve from 0.93182

Epoch 19: val_accuracy did not improve from 0.93182

Epoch 20: val_accuracy did not improve from 0.93182

Epoch 21: val_accuracy did not improve from 0.93182

Epoch 22: val_accuracy did not improve from 0.93182

Epoch 23: val_accuracy did not improve from 0.93182

Epoch 24: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.4077 - accuracy: 0.8182 - 35ms/epoch - 18ms/step


#######################################################


the model mod90 use a learning rate = 7, l2 regularization = 2 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.77273 to 0.88636, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4023 - accuracy: 0.8636 - 34ms/epoch - 17ms/step


#######################################################


the model mod91 use a learning rate = 8, l2 regularization = 2 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.88636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.88636

Epoch 3: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.4366 - accuracy: 0.8409 - 34ms/epoch - 17ms/step


#######################################################


the model mod92 use a learning rate = 9, l2 regularization = 2 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.79545

Epoch 3: val_accuracy improved from 0.79545 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4378 - accuracy: 0.8636 - 34ms/epoch - 17ms/step


#######################################################


the model mod93 use a learning rate = 0, l2 regularization = 3 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.79545

Epoch 3: val_accuracy did not improve from 0.79545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.79545

Epoch 5: val_accuracy did not improve from 0.79545

Epoch 6: val_accuracy did not improve from 0.79545

Epoch 7: val_accuracy did not improve from 0.79545

Epoch 8: val_accuracy did not improve from 0.79545

Epoch 9: val_accuracy did not improve from 0.79545

Epoch 10: val_accuracy did not improve from 0.79545

Epoch 11: val_accuracy did not improve from 0.79545

Epoch 12: val_accuracy did not improve from 0.79545

Epoch 13: val_accuracy did not improve from 0.79545

Epoch 14: val_accuracy did not improve from 0.79545

Epoch 15: val_accuracy did not improve from 0.79545

Epoch 16: val_accuracy did not improve from 0.79545

Epoch 17: val_accuracy did not improve from 0.79545

Epoch 18: val_accuracy did not improve from 0.79545

Epoch 19: val_accuracy did not improve from 0.79545

Epoch 20: val_accuracy did not improve from 0.79545

Epoch 21: val_accuracy did not improve from 0.79545
2/2 - 0s - loss: 0.6885 - accuracy: 0.6136 - 37ms/epoch - 18ms/step


#######################################################


the model mod94 use a learning rate = 1, l2 regularization = 3 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.88636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.88636

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 1.0298 - accuracy: 0.8182 - 43ms/epoch - 21ms/step


#######################################################


the model mod95 use a learning rate = 2, l2 regularization = 3 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.56818 to 0.75000, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.75000 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.81818

Epoch 6: val_accuracy did not improve from 0.81818

Epoch 7: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 9: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy did not improve from 0.88636

Epoch 28: val_accuracy did not improve from 0.88636

Epoch 29: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4361 - accuracy: 0.8409 - 48ms/epoch - 24ms/step


#######################################################


the model mod96 use a learning rate = 3, l2 regularization = 3 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.45455, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.47727

Epoch 4: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.52273 to 0.54545, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.54545 to 0.59091, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 9: val_accuracy improved from 0.61364 to 0.65909, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.65909

Epoch 11: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5

Epoch 12: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.72727

Epoch 14: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5

Epoch 15: val_accuracy did not improve from 0.77273

Epoch 16: val_accuracy improved from 0.77273 to 0.81818, saving model to best_model.h5

Epoch 17: val_accuracy improved from 0.81818 to 0.86364, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364

Epoch 23: val_accuracy did not improve from 0.86364

Epoch 24: val_accuracy did not improve from 0.86364

Epoch 25: val_accuracy did not improve from 0.86364

Epoch 26: val_accuracy did not improve from 0.86364

Epoch 27: val_accuracy did not improve from 0.86364

Epoch 28: val_accuracy did not improve from 0.86364

Epoch 29: val_accuracy did not improve from 0.86364

Epoch 30: val_accuracy did not improve from 0.86364

Epoch 31: val_accuracy did not improve from 0.86364

Epoch 32: val_accuracy did not improve from 0.86364

Epoch 33: val_accuracy did not improve from 0.86364

Epoch 34: val_accuracy did not improve from 0.86364

Epoch 35: val_accuracy did not improve from 0.86364

Epoch 36: val_accuracy did not improve from 0.86364

Epoch 37: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.4527 - accuracy: 0.8409 - 33ms/epoch - 17ms/step


#######################################################


the model mod97 use a learning rate = 4, l2 regularization = 3 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.61364, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.61364 to 0.68182, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.72727 to 0.79545, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5

Epoch 23: val_accuracy did not improve from 0.93182

Epoch 24: val_accuracy did not improve from 0.93182

Epoch 25: val_accuracy did not improve from 0.93182

Epoch 26: val_accuracy did not improve from 0.93182

Epoch 27: val_accuracy did not improve from 0.93182

Epoch 28: val_accuracy did not improve from 0.93182

Epoch 29: val_accuracy did not improve from 0.93182

Epoch 30: val_accuracy did not improve from 0.93182

Epoch 31: val_accuracy did not improve from 0.93182

Epoch 32: val_accuracy did not improve from 0.93182

Epoch 33: val_accuracy did not improve from 0.93182

Epoch 34: val_accuracy did not improve from 0.93182

Epoch 35: val_accuracy did not improve from 0.93182

Epoch 36: val_accuracy did not improve from 0.93182

Epoch 37: val_accuracy did not improve from 0.93182

Epoch 38: val_accuracy did not improve from 0.93182

Epoch 39: val_accuracy did not improve from 0.93182

Epoch 40: val_accuracy did not improve from 0.93182

Epoch 41: val_accuracy did not improve from 0.93182

Epoch 42: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.3593 - accuracy: 0.8864 - 61ms/epoch - 31ms/step


#######################################################


the model mod98 use a learning rate = 5, l2 regularization = 3 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.54545

Epoch 4: val_accuracy improved from 0.54545 to 0.59091, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.59091

Epoch 6: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.61364 to 0.68182, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5

Epoch 9: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.75000

Epoch 11: val_accuracy did not improve from 0.75000

Epoch 12: val_accuracy did not improve from 0.75000

Epoch 13: val_accuracy did not improve from 0.75000

Epoch 14: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 15: val_accuracy did not improve from 0.77273

Epoch 16: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 17: val_accuracy did not improve from 0.79545

Epoch 18: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 19: val_accuracy did not improve from 0.81818

Epoch 20: val_accuracy did not improve from 0.81818

Epoch 21: val_accuracy did not improve from 0.81818

Epoch 22: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 23: val_accuracy did not improve from 0.84091

Epoch 24: val_accuracy did not improve from 0.84091

Epoch 25: val_accuracy did not improve from 0.84091

Epoch 26: val_accuracy did not improve from 0.84091

Epoch 27: val_accuracy did not improve from 0.84091

Epoch 28: val_accuracy did not improve from 0.84091

Epoch 29: val_accuracy did not improve from 0.84091

Epoch 30: val_accuracy did not improve from 0.84091

Epoch 31: val_accuracy did not improve from 0.84091

Epoch 32: val_accuracy did not improve from 0.84091

Epoch 33: val_accuracy did not improve from 0.84091

Epoch 34: val_accuracy did not improve from 0.84091

Epoch 35: val_accuracy did not improve from 0.84091

Epoch 36: val_accuracy did not improve from 0.84091

Epoch 37: val_accuracy did not improve from 0.84091

Epoch 38: val_accuracy did not improve from 0.84091

Epoch 39: val_accuracy did not improve from 0.84091

Epoch 40: val_accuracy did not improve from 0.84091

Epoch 41: val_accuracy did not improve from 0.84091

Epoch 42: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.3789 - accuracy: 0.8182 - 40ms/epoch - 20ms/step


#######################################################


the model mod99 use a learning rate = 6, l2 regularization = 3 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.36364

Epoch 3: val_accuracy did not improve from 0.36364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.36364

Epoch 5: val_accuracy improved from 0.36364 to 0.38636, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.38636

Epoch 7: val_accuracy did not improve from 0.38636

Epoch 8: val_accuracy did not improve from 0.38636

Epoch 9: val_accuracy did not improve from 0.38636

Epoch 10: val_accuracy did not improve from 0.38636

Epoch 11: val_accuracy did not improve from 0.38636

Epoch 12: val_accuracy did not improve from 0.38636

Epoch 13: val_accuracy did not improve from 0.38636

Epoch 14: val_accuracy did not improve from 0.38636

Epoch 15: val_accuracy did not improve from 0.38636

Epoch 16: val_accuracy did not improve from 0.38636

Epoch 17: val_accuracy did not improve from 0.38636

Epoch 18: val_accuracy did not improve from 0.38636

Epoch 19: val_accuracy did not improve from 0.38636

Epoch 20: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5

Epoch 21: val_accuracy did not improve from 0.40909

Epoch 22: val_accuracy did not improve from 0.40909

Epoch 23: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5

Epoch 24: val_accuracy did not improve from 0.43182

Epoch 25: val_accuracy did not improve from 0.43182

Epoch 26: val_accuracy did not improve from 0.43182

Epoch 27: val_accuracy did not improve from 0.43182

Epoch 28: val_accuracy did not improve from 0.43182

Epoch 29: val_accuracy did not improve from 0.43182

Epoch 30: val_accuracy did not improve from 0.43182

Epoch 31: val_accuracy did not improve from 0.43182

Epoch 32: val_accuracy did not improve from 0.43182

Epoch 33: val_accuracy did not improve from 0.43182

Epoch 34: val_accuracy did not improve from 0.43182

Epoch 35: val_accuracy did not improve from 0.43182

Epoch 36: val_accuracy did not improve from 0.43182

Epoch 37: val_accuracy did not improve from 0.43182

Epoch 38: val_accuracy did not improve from 0.43182

Epoch 39: val_accuracy did not improve from 0.43182

Epoch 40: val_accuracy did not improve from 0.43182

Epoch 41: val_accuracy did not improve from 0.43182

Epoch 42: val_accuracy did not improve from 0.43182

Epoch 43: val_accuracy did not improve from 0.43182
2/2 - 0s - loss: 0.9472 - accuracy: 0.4318 - 34ms/epoch - 17ms/step


#######################################################


the model mod100 use a learning rate = 7, l2 regularization = 3 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.59091

Epoch 3: val_accuracy did not improve from 0.59091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.59091

Epoch 5: val_accuracy did not improve from 0.59091

Epoch 6: val_accuracy did not improve from 0.59091

Epoch 7: val_accuracy did not improve from 0.59091

Epoch 8: val_accuracy did not improve from 0.59091

Epoch 9: val_accuracy did not improve from 0.59091

Epoch 10: val_accuracy did not improve from 0.59091

Epoch 11: val_accuracy did not improve from 0.59091

Epoch 12: val_accuracy did not improve from 0.59091

Epoch 13: val_accuracy did not improve from 0.59091

Epoch 14: val_accuracy did not improve from 0.59091

Epoch 15: val_accuracy did not improve from 0.59091

Epoch 16: val_accuracy did not improve from 0.59091

Epoch 17: val_accuracy did not improve from 0.59091

Epoch 18: val_accuracy did not improve from 0.59091

Epoch 19: val_accuracy did not improve from 0.59091

Epoch 20: val_accuracy did not improve from 0.59091

Epoch 21: val_accuracy did not improve from 0.59091
2/2 - 0s - loss: 0.6852 - accuracy: 0.5909 - 42ms/epoch - 21ms/step


#######################################################


the model mod101 use a learning rate = 8, l2 regularization = 3 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.65909, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.65909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.65909

Epoch 4: val_accuracy did not improve from 0.65909

Epoch 5: val_accuracy did not improve from 0.65909

Epoch 6: val_accuracy did not improve from 0.65909

Epoch 7: val_accuracy did not improve from 0.65909

Epoch 8: val_accuracy did not improve from 0.65909

Epoch 9: val_accuracy did not improve from 0.65909

Epoch 10: val_accuracy did not improve from 0.65909

Epoch 11: val_accuracy did not improve from 0.65909

Epoch 12: val_accuracy did not improve from 0.65909

Epoch 13: val_accuracy did not improve from 0.65909

Epoch 14: val_accuracy did not improve from 0.65909

Epoch 15: val_accuracy did not improve from 0.65909

Epoch 16: val_accuracy did not improve from 0.65909

Epoch 17: val_accuracy did not improve from 0.65909

Epoch 18: val_accuracy did not improve from 0.65909

Epoch 19: val_accuracy did not improve from 0.65909

Epoch 20: val_accuracy did not improve from 0.65909

Epoch 21: val_accuracy did not improve from 0.65909
2/2 - 0s - loss: 0.7369 - accuracy: 0.6591 - 34ms/epoch - 17ms/step


#######################################################


the model mod102 use a learning rate = 9, l2 regularization = 3 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.75000

Epoch 3: val_accuracy did not improve from 0.75000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.75000

Epoch 5: val_accuracy did not improve from 0.75000

Epoch 6: val_accuracy did not improve from 0.75000

Epoch 7: val_accuracy did not improve from 0.75000

Epoch 8: val_accuracy did not improve from 0.75000

Epoch 9: val_accuracy did not improve from 0.75000

Epoch 10: val_accuracy did not improve from 0.75000

Epoch 11: val_accuracy did not improve from 0.75000

Epoch 12: val_accuracy did not improve from 0.75000

Epoch 13: val_accuracy did not improve from 0.75000

Epoch 14: val_accuracy did not improve from 0.75000

Epoch 15: val_accuracy did not improve from 0.75000

Epoch 16: val_accuracy did not improve from 0.75000

Epoch 17: val_accuracy did not improve from 0.75000

Epoch 18: val_accuracy did not improve from 0.75000

Epoch 19: val_accuracy did not improve from 0.75000

Epoch 20: val_accuracy did not improve from 0.75000

Epoch 21: val_accuracy did not improve from 0.75000
2/2 - 0s - loss: 0.7096 - accuracy: 0.7500 - 47ms/epoch - 24ms/step


#######################################################


the model mod103 use a learning rate = 0, l2 regularization = 3 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.81818

Epoch 3: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.8025 - accuracy: 0.7500 - 48ms/epoch - 24ms/step


#######################################################


the model mod104 use a learning rate = 1, l2 regularization = 3 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.72727 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.79545 to 0.84091, saving model to best_model.h5

Epoch 4: val_accuracy did not improve from 0.84091

Epoch 5: val_accuracy did not improve from 0.84091

Epoch 6: val_accuracy did not improve from 0.84091

Epoch 7: val_accuracy did not improve from 0.84091

Epoch 8: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909

Epoch 35: val_accuracy did not improve from 0.90909

Epoch 36: val_accuracy did not improve from 0.90909

Epoch 37: val_accuracy did not improve from 0.90909

Epoch 38: val_accuracy did not improve from 0.90909

Epoch 39: val_accuracy did not improve from 0.90909

Epoch 40: val_accuracy did not improve from 0.90909

Epoch 41: val_accuracy did not improve from 0.90909

Epoch 42: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3152 - accuracy: 0.9091 - 42ms/epoch - 21ms/step


#######################################################


the model mod105 use a learning rate = 2, l2 regularization = 3 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.75000

Epoch 5: val_accuracy improved from 0.75000 to 0.79545, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.79545

Epoch 7: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.81818

Epoch 9: val_accuracy did not improve from 0.81818

Epoch 10: val_accuracy did not improve from 0.81818

Epoch 11: val_accuracy did not improve from 0.81818

Epoch 12: val_accuracy did not improve from 0.81818

Epoch 13: val_accuracy did not improve from 0.81818

Epoch 14: val_accuracy did not improve from 0.81818

Epoch 15: val_accuracy did not improve from 0.81818

Epoch 16: val_accuracy did not improve from 0.81818

Epoch 17: val_accuracy did not improve from 0.81818

Epoch 18: val_accuracy did not improve from 0.81818

Epoch 19: val_accuracy did not improve from 0.81818

Epoch 20: val_accuracy did not improve from 0.81818

Epoch 21: val_accuracy did not improve from 0.81818

Epoch 22: val_accuracy did not improve from 0.81818

Epoch 23: val_accuracy did not improve from 0.81818

Epoch 24: val_accuracy did not improve from 0.81818

Epoch 25: val_accuracy did not improve from 0.81818

Epoch 26: val_accuracy did not improve from 0.81818

Epoch 27: val_accuracy did not improve from 0.81818
2/2 - 0s - loss: 0.5449 - accuracy: 0.7955 - 36ms/epoch - 18ms/step


#######################################################


the model mod106 use a learning rate = 3, l2 regularization = 3 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.56818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.56818

Epoch 4: val_accuracy did not improve from 0.56818

Epoch 5: val_accuracy did not improve from 0.56818

Epoch 6: val_accuracy did not improve from 0.56818

Epoch 7: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.59091

Epoch 9: val_accuracy did not improve from 0.59091

Epoch 10: val_accuracy did not improve from 0.59091

Epoch 11: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.61364

Epoch 13: val_accuracy did not improve from 0.61364

Epoch 14: val_accuracy did not improve from 0.61364

Epoch 15: val_accuracy did not improve from 0.61364

Epoch 16: val_accuracy did not improve from 0.61364

Epoch 17: val_accuracy did not improve from 0.61364

Epoch 18: val_accuracy did not improve from 0.61364

Epoch 19: val_accuracy did not improve from 0.61364

Epoch 20: val_accuracy did not improve from 0.61364

Epoch 21: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5

Epoch 22: val_accuracy did not improve from 0.63636

Epoch 23: val_accuracy did not improve from 0.63636

Epoch 24: val_accuracy did not improve from 0.63636

Epoch 25: val_accuracy did not improve from 0.63636

Epoch 26: val_accuracy improved from 0.63636 to 0.68182, saving model to best_model.h5

Epoch 27: val_accuracy did not improve from 0.68182

Epoch 28: val_accuracy did not improve from 0.68182

Epoch 29: val_accuracy did not improve from 0.68182

Epoch 30: val_accuracy did not improve from 0.68182

Epoch 31: val_accuracy did not improve from 0.68182

Epoch 32: val_accuracy did not improve from 0.68182

Epoch 33: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 34: val_accuracy did not improve from 0.70455

Epoch 35: val_accuracy did not improve from 0.70455

Epoch 36: val_accuracy did not improve from 0.70455

Epoch 37: val_accuracy did not improve from 0.70455

Epoch 38: val_accuracy did not improve from 0.70455

Epoch 39: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 40: val_accuracy did not improve from 0.72727

Epoch 41: val_accuracy did not improve from 0.72727

Epoch 42: val_accuracy did not improve from 0.72727

Epoch 43: val_accuracy did not improve from 0.72727

Epoch 44: val_accuracy did not improve from 0.72727

Epoch 45: val_accuracy did not improve from 0.72727

Epoch 46: val_accuracy did not improve from 0.72727

Epoch 47: val_accuracy did not improve from 0.72727

Epoch 48: val_accuracy did not improve from 0.72727

Epoch 49: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 50: val_accuracy did not improve from 0.75000

Epoch 51: val_accuracy did not improve from 0.75000

Epoch 52: val_accuracy did not improve from 0.75000

Epoch 53: val_accuracy did not improve from 0.75000

Epoch 54: val_accuracy did not improve from 0.75000

Epoch 55: val_accuracy did not improve from 0.75000

Epoch 56: val_accuracy did not improve from 0.75000

Epoch 57: val_accuracy did not improve from 0.75000

Epoch 58: val_accuracy did not improve from 0.75000

Epoch 59: val_accuracy did not improve from 0.75000

Epoch 60: val_accuracy did not improve from 0.75000

Epoch 61: val_accuracy did not improve from 0.75000

Epoch 62: val_accuracy did not improve from 0.75000

Epoch 63: val_accuracy did not improve from 0.75000

Epoch 64: val_accuracy did not improve from 0.75000

Epoch 65: val_accuracy did not improve from 0.75000

Epoch 66: val_accuracy did not improve from 0.75000

Epoch 67: val_accuracy did not improve from 0.75000

Epoch 68: val_accuracy did not improve from 0.75000

Epoch 69: val_accuracy did not improve from 0.75000
2/2 - 0s - loss: 0.7531 - accuracy: 0.7500 - 42ms/epoch - 21ms/step


#######################################################


the model mod107 use a learning rate = 4, l2 regularization = 3 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.61364, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.61364

Epoch 3: val_accuracy did not improve from 0.61364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.63636

Epoch 6: val_accuracy did not improve from 0.63636

Epoch 7: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.68182

Epoch 10: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.70455

Epoch 12: val_accuracy did not improve from 0.70455

Epoch 13: val_accuracy did not improve from 0.70455

Epoch 14: val_accuracy did not improve from 0.70455

Epoch 15: val_accuracy did not improve from 0.70455

Epoch 16: val_accuracy did not improve from 0.70455

Epoch 17: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.72727

Epoch 19: val_accuracy did not improve from 0.72727

Epoch 20: val_accuracy did not improve from 0.72727

Epoch 21: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 22: val_accuracy did not improve from 0.75000

Epoch 23: val_accuracy did not improve from 0.75000

Epoch 24: val_accuracy did not improve from 0.75000

Epoch 25: val_accuracy did not improve from 0.75000

Epoch 26: val_accuracy did not improve from 0.75000

Epoch 27: val_accuracy did not improve from 0.75000

Epoch 28: val_accuracy did not improve from 0.75000

Epoch 29: val_accuracy did not improve from 0.75000

Epoch 30: val_accuracy did not improve from 0.75000

Epoch 31: val_accuracy did not improve from 0.75000

Epoch 32: val_accuracy did not improve from 0.75000

Epoch 33: val_accuracy did not improve from 0.75000

Epoch 34: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 35: val_accuracy did not improve from 0.77273

Epoch 36: val_accuracy did not improve from 0.77273

Epoch 37: val_accuracy did not improve from 0.77273

Epoch 38: val_accuracy did not improve from 0.77273

Epoch 39: val_accuracy did not improve from 0.77273

Epoch 40: val_accuracy did not improve from 0.77273

Epoch 41: val_accuracy did not improve from 0.77273

Epoch 42: val_accuracy did not improve from 0.77273

Epoch 43: val_accuracy did not improve from 0.77273

Epoch 44: val_accuracy did not improve from 0.77273

Epoch 45: val_accuracy did not improve from 0.77273

Epoch 46: val_accuracy did not improve from 0.77273

Epoch 47: val_accuracy did not improve from 0.77273

Epoch 48: val_accuracy did not improve from 0.77273

Epoch 49: val_accuracy did not improve from 0.77273

Epoch 50: val_accuracy did not improve from 0.77273

Epoch 51: val_accuracy did not improve from 0.77273

Epoch 52: val_accuracy did not improve from 0.77273

Epoch 53: val_accuracy did not improve from 0.77273

Epoch 54: val_accuracy did not improve from 0.77273
2/2 - 0s - loss: 0.5420 - accuracy: 0.7727 - 43ms/epoch - 21ms/step


#######################################################


the model mod108 use a learning rate = 5, l2 regularization = 3 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.52273, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.52273
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.52273

Epoch 4: val_accuracy did not improve from 0.52273

Epoch 5: val_accuracy did not improve from 0.52273

Epoch 6: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.56818

Epoch 8: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.59091

Epoch 10: val_accuracy did not improve from 0.59091

Epoch 11: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.61364

Epoch 13: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5

Epoch 14: val_accuracy did not improve from 0.63636

Epoch 15: val_accuracy did not improve from 0.63636

Epoch 16: val_accuracy did not improve from 0.63636

Epoch 17: val_accuracy did not improve from 0.63636

Epoch 18: val_accuracy did not improve from 0.63636

Epoch 19: val_accuracy did not improve from 0.63636

Epoch 20: val_accuracy did not improve from 0.63636

Epoch 21: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 22: val_accuracy did not improve from 0.65909

Epoch 23: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5

Epoch 24: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 25: val_accuracy did not improve from 0.70455

Epoch 26: val_accuracy did not improve from 0.70455

Epoch 27: val_accuracy did not improve from 0.70455

Epoch 28: val_accuracy did not improve from 0.70455

Epoch 29: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 30: val_accuracy did not improve from 0.72727

Epoch 31: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 32: val_accuracy did not improve from 0.75000

Epoch 33: val_accuracy did not improve from 0.75000

Epoch 34: val_accuracy did not improve from 0.75000

Epoch 35: val_accuracy did not improve from 0.75000

Epoch 36: val_accuracy did not improve from 0.75000

Epoch 37: val_accuracy did not improve from 0.75000

Epoch 38: val_accuracy did not improve from 0.75000

Epoch 39: val_accuracy did not improve from 0.75000

Epoch 40: val_accuracy did not improve from 0.75000

Epoch 41: val_accuracy did not improve from 0.75000

Epoch 42: val_accuracy did not improve from 0.75000

Epoch 43: val_accuracy did not improve from 0.75000

Epoch 44: val_accuracy did not improve from 0.75000

Epoch 45: val_accuracy did not improve from 0.75000

Epoch 46: val_accuracy did not improve from 0.75000

Epoch 47: val_accuracy did not improve from 0.75000

Epoch 48: val_accuracy did not improve from 0.75000

Epoch 49: val_accuracy did not improve from 0.75000

Epoch 50: val_accuracy did not improve from 0.75000

Epoch 51: val_accuracy did not improve from 0.75000
2/2 - 0s - loss: 0.6408 - accuracy: 0.7500 - 38ms/epoch - 19ms/step


#######################################################


the model mod109 use a learning rate = 6, l2 regularization = 3 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.38636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.38636

Epoch 3: val_accuracy did not improve from 0.38636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.38636

Epoch 5: val_accuracy did not improve from 0.38636

Epoch 6: val_accuracy did not improve from 0.38636

Epoch 7: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.40909

Epoch 9: val_accuracy did not improve from 0.40909

Epoch 10: val_accuracy did not improve from 0.40909

Epoch 11: val_accuracy did not improve from 0.40909

Epoch 12: val_accuracy did not improve from 0.40909

Epoch 13: val_accuracy did not improve from 0.40909

Epoch 14: val_accuracy did not improve from 0.40909

Epoch 15: val_accuracy did not improve from 0.40909

Epoch 16: val_accuracy did not improve from 0.40909

Epoch 17: val_accuracy did not improve from 0.40909

Epoch 18: val_accuracy did not improve from 0.40909

Epoch 19: val_accuracy did not improve from 0.40909

Epoch 20: val_accuracy did not improve from 0.40909

Epoch 21: val_accuracy did not improve from 0.40909

Epoch 22: val_accuracy did not improve from 0.40909

Epoch 23: val_accuracy did not improve from 0.40909

Epoch 24: val_accuracy did not improve from 0.40909

Epoch 25: val_accuracy did not improve from 0.40909

Epoch 26: val_accuracy did not improve from 0.40909

Epoch 27: val_accuracy did not improve from 0.40909
2/2 - 0s - loss: 0.9166 - accuracy: 0.4091 - 36ms/epoch - 18ms/step


#######################################################


the model mod110 use a learning rate = 7, l2 regularization = 3 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.34091, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.34091

Epoch 3: val_accuracy did not improve from 0.34091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.34091

Epoch 5: val_accuracy did not improve from 0.34091

Epoch 6: val_accuracy did not improve from 0.34091

Epoch 7: val_accuracy did not improve from 0.34091

Epoch 8: val_accuracy did not improve from 0.34091

Epoch 9: val_accuracy did not improve from 0.34091

Epoch 10: val_accuracy did not improve from 0.34091

Epoch 11: val_accuracy did not improve from 0.34091

Epoch 12: val_accuracy did not improve from 0.34091

Epoch 13: val_accuracy did not improve from 0.34091

Epoch 14: val_accuracy did not improve from 0.34091

Epoch 15: val_accuracy did not improve from 0.34091

Epoch 16: val_accuracy did not improve from 0.34091

Epoch 17: val_accuracy did not improve from 0.34091

Epoch 18: val_accuracy did not improve from 0.34091

Epoch 19: val_accuracy did not improve from 0.34091

Epoch 20: val_accuracy did not improve from 0.34091

Epoch 21: val_accuracy did not improve from 0.34091
2/2 - 0s - loss: 0.9174 - accuracy: 0.3409 - 39ms/epoch - 20ms/step


#######################################################


the model mod111 use a learning rate = 8, l2 regularization = 3 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.45455, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.45455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.45455

Epoch 4: val_accuracy did not improve from 0.45455

Epoch 5: val_accuracy did not improve from 0.45455

Epoch 6: val_accuracy did not improve from 0.45455

Epoch 7: val_accuracy did not improve from 0.45455

Epoch 8: val_accuracy did not improve from 0.45455

Epoch 9: val_accuracy did not improve from 0.45455

Epoch 10: val_accuracy did not improve from 0.45455

Epoch 11: val_accuracy did not improve from 0.45455

Epoch 12: val_accuracy did not improve from 0.45455

Epoch 13: val_accuracy did not improve from 0.45455

Epoch 14: val_accuracy did not improve from 0.45455

Epoch 15: val_accuracy did not improve from 0.45455

Epoch 16: val_accuracy did not improve from 0.45455

Epoch 17: val_accuracy did not improve from 0.45455

Epoch 18: val_accuracy did not improve from 0.45455

Epoch 19: val_accuracy did not improve from 0.45455

Epoch 20: val_accuracy did not improve from 0.45455

Epoch 21: val_accuracy did not improve from 0.45455
2/2 - 0s - loss: 0.8333 - accuracy: 0.4545 - 37ms/epoch - 18ms/step


#######################################################


the model mod112 use a learning rate = 9, l2 regularization = 3 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.63636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.63636

Epoch 3: val_accuracy did not improve from 0.63636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.63636

Epoch 5: val_accuracy did not improve from 0.63636

Epoch 6: val_accuracy did not improve from 0.63636

Epoch 7: val_accuracy did not improve from 0.63636

Epoch 8: val_accuracy did not improve from 0.63636

Epoch 9: val_accuracy did not improve from 0.63636

Epoch 10: val_accuracy did not improve from 0.63636

Epoch 11: val_accuracy did not improve from 0.63636

Epoch 12: val_accuracy did not improve from 0.63636

Epoch 13: val_accuracy did not improve from 0.63636

Epoch 14: val_accuracy did not improve from 0.63636

Epoch 15: val_accuracy did not improve from 0.63636

Epoch 16: val_accuracy did not improve from 0.63636

Epoch 17: val_accuracy did not improve from 0.63636

Epoch 18: val_accuracy did not improve from 0.63636

Epoch 19: val_accuracy did not improve from 0.63636

Epoch 20: val_accuracy did not improve from 0.63636

Epoch 21: val_accuracy did not improve from 0.63636
2/2 - 0s - loss: 0.7298 - accuracy: 0.6364 - 37ms/epoch - 19ms/step


#######################################################


the model mod113 use a learning rate = 0, l2 regularization = 3 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.59091 to 0.79545, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.79545 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5716 - accuracy: 0.7955 - 43ms/epoch - 21ms/step


#######################################################


the model mod114 use a learning rate = 1, l2 regularization = 3 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.77273 to 0.84091, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.84091

Epoch 5: val_accuracy did not improve from 0.84091

Epoch 6: val_accuracy did not improve from 0.84091

Epoch 7: val_accuracy did not improve from 0.84091

Epoch 8: val_accuracy did not improve from 0.84091

Epoch 9: val_accuracy did not improve from 0.84091

Epoch 10: val_accuracy did not improve from 0.84091

Epoch 11: val_accuracy did not improve from 0.84091

Epoch 12: val_accuracy did not improve from 0.84091

Epoch 13: val_accuracy did not improve from 0.84091

Epoch 14: val_accuracy did not improve from 0.84091

Epoch 15: val_accuracy did not improve from 0.84091

Epoch 16: val_accuracy did not improve from 0.84091

Epoch 17: val_accuracy did not improve from 0.84091

Epoch 18: val_accuracy did not improve from 0.84091

Epoch 19: val_accuracy did not improve from 0.84091

Epoch 20: val_accuracy did not improve from 0.84091

Epoch 21: val_accuracy did not improve from 0.84091

Epoch 22: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.7465 - accuracy: 0.8182 - 36ms/epoch - 18ms/step


#######################################################


the model mod115 use a learning rate = 2, l2 regularization = 3 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.86364, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.86364

Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.86364

Epoch 5: val_accuracy did not improve from 0.86364

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.6591 - accuracy: 0.8182 - 35ms/epoch - 17ms/step


#######################################################


the model mod116 use a learning rate = 3, l2 regularization = 3 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.77273

Epoch 3: val_accuracy improved from 0.77273 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.84091

Epoch 5: val_accuracy did not improve from 0.84091

Epoch 6: val_accuracy did not improve from 0.84091

Epoch 7: val_accuracy did not improve from 0.84091

Epoch 8: val_accuracy did not improve from 0.84091

Epoch 9: val_accuracy did not improve from 0.84091

Epoch 10: val_accuracy did not improve from 0.84091

Epoch 11: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364

Epoch 23: val_accuracy did not improve from 0.86364

Epoch 24: val_accuracy did not improve from 0.86364

Epoch 25: val_accuracy did not improve from 0.86364

Epoch 26: val_accuracy did not improve from 0.86364

Epoch 27: val_accuracy did not improve from 0.86364

Epoch 28: val_accuracy did not improve from 0.86364

Epoch 29: val_accuracy did not improve from 0.86364

Epoch 30: val_accuracy did not improve from 0.86364

Epoch 31: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.5094 - accuracy: 0.8182 - 47ms/epoch - 23ms/step


#######################################################


the model mod117 use a learning rate = 4, l2 regularization = 3 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.81818 to 0.88636, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5067 - accuracy: 0.7727 - 53ms/epoch - 27ms/step


#######################################################


the model mod118 use a learning rate = 5, l2 regularization = 3 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.88636

Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5352 - accuracy: 0.8409 - 46ms/epoch - 23ms/step


#######################################################


the model mod119 use a learning rate = 6, l2 regularization = 3 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 4: val_accuracy did not improve from 0.90909

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.5192 - accuracy: 0.8864 - 50ms/epoch - 25ms/step


#######################################################


the model mod120 use a learning rate = 7, l2 regularization = 3 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.81818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 4: val_accuracy did not improve from 0.84091

Epoch 5: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy did not improve from 0.88636

Epoch 28: val_accuracy did not improve from 0.88636

Epoch 29: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5476 - accuracy: 0.8636 - 34ms/epoch - 17ms/step


#######################################################


the model mod121 use a learning rate = 8, l2 regularization = 3 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.77273

Epoch 3: val_accuracy improved from 0.77273 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.86364

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364

Epoch 23: val_accuracy did not improve from 0.86364

Epoch 24: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.6529 - accuracy: 0.7500 - 41ms/epoch - 20ms/step


#######################################################


the model mod122 use a learning rate = 9, l2 regularization = 3 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.68182 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 4: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.6571 - accuracy: 0.7955 - 50ms/epoch - 25ms/step


#######################################################


the model mod123 use a learning rate = 0, l2 regularization = 4 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.50000 to 0.79545, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.79545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.79545

Epoch 5: val_accuracy did not improve from 0.79545

Epoch 6: val_accuracy did not improve from 0.79545

Epoch 7: val_accuracy did not improve from 0.79545

Epoch 8: val_accuracy did not improve from 0.79545

Epoch 9: val_accuracy did not improve from 0.79545

Epoch 10: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.81818

Epoch 12: val_accuracy did not improve from 0.81818

Epoch 13: val_accuracy did not improve from 0.81818

Epoch 14: val_accuracy did not improve from 0.81818

Epoch 15: val_accuracy did not improve from 0.81818

Epoch 16: val_accuracy did not improve from 0.81818

Epoch 17: val_accuracy did not improve from 0.81818

Epoch 18: val_accuracy did not improve from 0.81818

Epoch 19: val_accuracy did not improve from 0.81818

Epoch 20: val_accuracy did not improve from 0.81818

Epoch 21: val_accuracy did not improve from 0.81818

Epoch 22: val_accuracy did not improve from 0.81818

Epoch 23: val_accuracy did not improve from 0.81818

Epoch 24: val_accuracy did not improve from 0.81818

Epoch 25: val_accuracy did not improve from 0.81818

Epoch 26: val_accuracy did not improve from 0.81818

Epoch 27: val_accuracy did not improve from 0.81818

Epoch 28: val_accuracy did not improve from 0.81818

Epoch 29: val_accuracy did not improve from 0.81818

Epoch 30: val_accuracy did not improve from 0.81818
2/2 - 0s - loss: 0.8507 - accuracy: 0.6818 - 41ms/epoch - 21ms/step


#######################################################


the model mod124 use a learning rate = 1, l2 regularization = 4 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.86364, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.86364

Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.86364

Epoch 5: val_accuracy did not improve from 0.86364

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 1.1400 - accuracy: 0.7955 - 38ms/epoch - 19ms/step


#######################################################


the model mod125 use a learning rate = 2, l2 regularization = 4 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.72727 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.93182

Epoch 11: val_accuracy did not improve from 0.93182

Epoch 12: val_accuracy did not improve from 0.93182

Epoch 13: val_accuracy did not improve from 0.93182

Epoch 14: val_accuracy did not improve from 0.93182

Epoch 15: val_accuracy did not improve from 0.93182

Epoch 16: val_accuracy did not improve from 0.93182

Epoch 17: val_accuracy did not improve from 0.93182

Epoch 18: val_accuracy did not improve from 0.93182

Epoch 19: val_accuracy did not improve from 0.93182

Epoch 20: val_accuracy did not improve from 0.93182

Epoch 21: val_accuracy did not improve from 0.93182

Epoch 22: val_accuracy did not improve from 0.93182

Epoch 23: val_accuracy did not improve from 0.93182

Epoch 24: val_accuracy did not improve from 0.93182

Epoch 25: val_accuracy did not improve from 0.93182

Epoch 26: val_accuracy did not improve from 0.93182

Epoch 27: val_accuracy did not improve from 0.93182

Epoch 28: val_accuracy did not improve from 0.93182

Epoch 29: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.4282 - accuracy: 0.8636 - 36ms/epoch - 18ms/step


#######################################################


the model mod126 use a learning rate = 3, l2 regularization = 4 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.43182, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.43182 to 0.47727, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.47727 to 0.59091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.61364 to 0.70455, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.70455

Epoch 7: val_accuracy improved from 0.70455 to 0.75000, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.77273

Epoch 10: val_accuracy did not improve from 0.77273

Epoch 11: val_accuracy did not improve from 0.77273

Epoch 12: val_accuracy did not improve from 0.77273

Epoch 13: val_accuracy did not improve from 0.77273

Epoch 14: val_accuracy did not improve from 0.77273

Epoch 15: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 16: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 17: val_accuracy did not improve from 0.81818

Epoch 18: val_accuracy did not improve from 0.81818

Epoch 19: val_accuracy did not improve from 0.81818

Epoch 20: val_accuracy did not improve from 0.81818

Epoch 21: val_accuracy did not improve from 0.81818

Epoch 22: val_accuracy did not improve from 0.81818

Epoch 23: val_accuracy did not improve from 0.81818

Epoch 24: val_accuracy did not improve from 0.81818

Epoch 25: val_accuracy did not improve from 0.81818

Epoch 26: val_accuracy did not improve from 0.81818

Epoch 27: val_accuracy did not improve from 0.81818

Epoch 28: val_accuracy did not improve from 0.81818

Epoch 29: val_accuracy did not improve from 0.81818

Epoch 30: val_accuracy did not improve from 0.81818

Epoch 31: val_accuracy did not improve from 0.81818

Epoch 32: val_accuracy did not improve from 0.81818

Epoch 33: val_accuracy did not improve from 0.81818

Epoch 34: val_accuracy improved from 0.81818 to 0.86364, saving model to best_model.h5

Epoch 35: val_accuracy did not improve from 0.86364

Epoch 36: val_accuracy did not improve from 0.86364

Epoch 37: val_accuracy did not improve from 0.86364

Epoch 38: val_accuracy did not improve from 0.86364

Epoch 39: val_accuracy did not improve from 0.86364

Epoch 40: val_accuracy did not improve from 0.86364

Epoch 41: val_accuracy did not improve from 0.86364

Epoch 42: val_accuracy did not improve from 0.86364

Epoch 43: val_accuracy did not improve from 0.86364

Epoch 44: val_accuracy did not improve from 0.86364

Epoch 45: val_accuracy did not improve from 0.86364

Epoch 46: val_accuracy did not improve from 0.86364

Epoch 47: val_accuracy did not improve from 0.86364

Epoch 48: val_accuracy did not improve from 0.86364

Epoch 49: val_accuracy did not improve from 0.86364

Epoch 50: val_accuracy did not improve from 0.86364

Epoch 51: val_accuracy did not improve from 0.86364

Epoch 52: val_accuracy did not improve from 0.86364

Epoch 53: val_accuracy did not improve from 0.86364

Epoch 54: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.3502 - accuracy: 0.8636 - 55ms/epoch - 27ms/step


#######################################################


the model mod127 use a learning rate = 4, l2 regularization = 4 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.25000, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.25000 to 0.34091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.34091 to 0.43182, saving model to best_model.h5

Epoch 4: val_accuracy improved from 0.43182 to 0.47727, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.47727 to 0.68182, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.68182 to 0.72727, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.72727 to 0.79545, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.79545 to 0.86364, saving model to best_model.h5

Epoch 9: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 10: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3542 - accuracy: 0.8864 - 33ms/epoch - 16ms/step


#######################################################


the model mod128 use a learning rate = 5, l2 regularization = 4 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.29545, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.29545 to 0.38636, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.38636 to 0.47727, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.47727 to 0.56818, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.61364 to 0.68182, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 9: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.72727

Epoch 11: val_accuracy did not improve from 0.72727

Epoch 12: val_accuracy did not improve from 0.72727

Epoch 13: val_accuracy did not improve from 0.72727

Epoch 14: val_accuracy did not improve from 0.72727

Epoch 15: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.75000

Epoch 17: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 18: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 19: val_accuracy did not improve from 0.79545

Epoch 20: val_accuracy did not improve from 0.79545

Epoch 21: val_accuracy did not improve from 0.79545

Epoch 22: val_accuracy did not improve from 0.79545

Epoch 23: val_accuracy did not improve from 0.79545

Epoch 24: val_accuracy did not improve from 0.79545

Epoch 25: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 26: val_accuracy did not improve from 0.81818

Epoch 27: val_accuracy did not improve from 0.81818

Epoch 28: val_accuracy did not improve from 0.81818

Epoch 29: val_accuracy did not improve from 0.81818

Epoch 30: val_accuracy did not improve from 0.81818

Epoch 31: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 32: val_accuracy did not improve from 0.84091

Epoch 33: val_accuracy did not improve from 0.84091

Epoch 34: val_accuracy did not improve from 0.84091

Epoch 35: val_accuracy did not improve from 0.84091

Epoch 36: val_accuracy did not improve from 0.84091

Epoch 37: val_accuracy did not improve from 0.84091

Epoch 38: val_accuracy did not improve from 0.84091

Epoch 39: val_accuracy did not improve from 0.84091

Epoch 40: val_accuracy did not improve from 0.84091

Epoch 41: val_accuracy did not improve from 0.84091

Epoch 42: val_accuracy did not improve from 0.84091

Epoch 43: val_accuracy did not improve from 0.84091

Epoch 44: val_accuracy did not improve from 0.84091

Epoch 45: val_accuracy did not improve from 0.84091

Epoch 46: val_accuracy did not improve from 0.84091

Epoch 47: val_accuracy did not improve from 0.84091

Epoch 48: val_accuracy did not improve from 0.84091

Epoch 49: val_accuracy did not improve from 0.84091

Epoch 50: val_accuracy did not improve from 0.84091

Epoch 51: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.3362 - accuracy: 0.8409 - 34ms/epoch - 17ms/step


#######################################################


the model mod129 use a learning rate = 6, l2 regularization = 4 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.27273, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.27273

Epoch 3: val_accuracy did not improve from 0.27273
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.27273

Epoch 5: val_accuracy did not improve from 0.27273

Epoch 6: val_accuracy did not improve from 0.27273

Epoch 7: val_accuracy did not improve from 0.27273

Epoch 8: val_accuracy did not improve from 0.27273

Epoch 9: val_accuracy did not improve from 0.27273

Epoch 10: val_accuracy did not improve from 0.27273

Epoch 11: val_accuracy did not improve from 0.27273

Epoch 12: val_accuracy did not improve from 0.27273

Epoch 13: val_accuracy did not improve from 0.27273

Epoch 14: val_accuracy did not improve from 0.27273

Epoch 15: val_accuracy did not improve from 0.27273

Epoch 16: val_accuracy did not improve from 0.27273

Epoch 17: val_accuracy did not improve from 0.27273

Epoch 18: val_accuracy improved from 0.27273 to 0.29545, saving model to best_model.h5

Epoch 19: val_accuracy did not improve from 0.29545

Epoch 20: val_accuracy did not improve from 0.29545

Epoch 21: val_accuracy did not improve from 0.29545

Epoch 22: val_accuracy did not improve from 0.29545

Epoch 23: val_accuracy did not improve from 0.29545

Epoch 24: val_accuracy did not improve from 0.29545

Epoch 25: val_accuracy did not improve from 0.29545

Epoch 26: val_accuracy did not improve from 0.29545

Epoch 27: val_accuracy did not improve from 0.29545

Epoch 28: val_accuracy did not improve from 0.29545

Epoch 29: val_accuracy did not improve from 0.29545

Epoch 30: val_accuracy did not improve from 0.29545

Epoch 31: val_accuracy improved from 0.29545 to 0.31818, saving model to best_model.h5

Epoch 32: val_accuracy did not improve from 0.31818

Epoch 33: val_accuracy did not improve from 0.31818

Epoch 34: val_accuracy did not improve from 0.31818

Epoch 35: val_accuracy did not improve from 0.31818

Epoch 36: val_accuracy did not improve from 0.31818

Epoch 37: val_accuracy did not improve from 0.31818

Epoch 38: val_accuracy improved from 0.31818 to 0.34091, saving model to best_model.h5

Epoch 39: val_accuracy improved from 0.34091 to 0.36364, saving model to best_model.h5

Epoch 40: val_accuracy did not improve from 0.36364

Epoch 41: val_accuracy did not improve from 0.36364

Epoch 42: val_accuracy did not improve from 0.36364

Epoch 43: val_accuracy did not improve from 0.36364

Epoch 44: val_accuracy did not improve from 0.36364

Epoch 45: val_accuracy did not improve from 0.36364

Epoch 46: val_accuracy did not improve from 0.36364

Epoch 47: val_accuracy did not improve from 0.36364

Epoch 48: val_accuracy did not improve from 0.36364

Epoch 49: val_accuracy improved from 0.36364 to 0.38636, saving model to best_model.h5

Epoch 50: val_accuracy did not improve from 0.38636

Epoch 51: val_accuracy did not improve from 0.38636

Epoch 52: val_accuracy did not improve from 0.38636

Epoch 53: val_accuracy did not improve from 0.38636

Epoch 54: val_accuracy did not improve from 0.38636

Epoch 55: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5

Epoch 56: val_accuracy did not improve from 0.40909

Epoch 57: val_accuracy did not improve from 0.40909

Epoch 58: val_accuracy did not improve from 0.40909

Epoch 59: val_accuracy did not improve from 0.40909

Epoch 60: val_accuracy did not improve from 0.40909

Epoch 61: val_accuracy did not improve from 0.40909

Epoch 62: val_accuracy did not improve from 0.40909

Epoch 63: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5

Epoch 64: val_accuracy did not improve from 0.43182

Epoch 65: val_accuracy did not improve from 0.43182

Epoch 66: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5

Epoch 67: val_accuracy did not improve from 0.45455

Epoch 68: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5

Epoch 69: val_accuracy did not improve from 0.47727

Epoch 70: val_accuracy did not improve from 0.47727

Epoch 71: val_accuracy did not improve from 0.47727

Epoch 72: val_accuracy did not improve from 0.47727

Epoch 73: val_accuracy did not improve from 0.47727

Epoch 74: val_accuracy did not improve from 0.47727

Epoch 75: val_accuracy did not improve from 0.47727

Epoch 76: val_accuracy did not improve from 0.47727

Epoch 77: val_accuracy did not improve from 0.47727

Epoch 78: val_accuracy did not improve from 0.47727

Epoch 79: val_accuracy did not improve from 0.47727

Epoch 80: val_accuracy did not improve from 0.47727

Epoch 81: val_accuracy did not improve from 0.47727

Epoch 82: val_accuracy did not improve from 0.47727

Epoch 83: val_accuracy did not improve from 0.47727

Epoch 84: val_accuracy did not improve from 0.47727

Epoch 85: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5

Epoch 86: val_accuracy did not improve from 0.50000

Epoch 87: val_accuracy did not improve from 0.50000

Epoch 88: val_accuracy did not improve from 0.50000

Epoch 89: val_accuracy did not improve from 0.50000

Epoch 90: val_accuracy did not improve from 0.50000

Epoch 91: val_accuracy did not improve from 0.50000

Epoch 92: val_accuracy did not improve from 0.50000

Epoch 93: val_accuracy did not improve from 0.50000

Epoch 94: val_accuracy did not improve from 0.50000

Epoch 95: val_accuracy did not improve from 0.50000

Epoch 96: val_accuracy did not improve from 0.50000

Epoch 97: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5

Epoch 98: val_accuracy improved from 0.52273 to 0.54545, saving model to best_model.h5

Epoch 99: val_accuracy did not improve from 0.54545

Epoch 100: val_accuracy did not improve from 0.54545

Epoch 101: val_accuracy did not improve from 0.54545

Epoch 102: val_accuracy did not improve from 0.54545

Epoch 103: val_accuracy did not improve from 0.54545

Epoch 104: val_accuracy did not improve from 0.54545

Epoch 105: val_accuracy did not improve from 0.54545

Epoch 106: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5

Epoch 107: val_accuracy did not improve from 0.56818

Epoch 108: val_accuracy did not improve from 0.56818

Epoch 109: val_accuracy did not improve from 0.56818

Epoch 110: val_accuracy did not improve from 0.56818

Epoch 111: val_accuracy did not improve from 0.56818

Epoch 112: val_accuracy did not improve from 0.56818

Epoch 113: val_accuracy did not improve from 0.56818

Epoch 114: val_accuracy did not improve from 0.56818

Epoch 115: val_accuracy did not improve from 0.56818

Epoch 116: val_accuracy did not improve from 0.56818

Epoch 117: val_accuracy did not improve from 0.56818

Epoch 118: val_accuracy did not improve from 0.56818

Epoch 119: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 120: val_accuracy did not improve from 0.59091

Epoch 121: val_accuracy did not improve from 0.59091

Epoch 122: val_accuracy did not improve from 0.59091

Epoch 123: val_accuracy did not improve from 0.59091

Epoch 124: val_accuracy did not improve from 0.59091

Epoch 125: val_accuracy did not improve from 0.59091

Epoch 126: val_accuracy did not improve from 0.59091

Epoch 127: val_accuracy did not improve from 0.59091

Epoch 128: val_accuracy did not improve from 0.59091

Epoch 129: val_accuracy did not improve from 0.59091

Epoch 130: val_accuracy did not improve from 0.59091

Epoch 131: val_accuracy did not improve from 0.59091

Epoch 132: val_accuracy did not improve from 0.59091

Epoch 133: val_accuracy did not improve from 0.59091

Epoch 134: val_accuracy did not improve from 0.59091

Epoch 135: val_accuracy did not improve from 0.59091

Epoch 136: val_accuracy did not improve from 0.59091

Epoch 137: val_accuracy did not improve from 0.59091

Epoch 138: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 139: val_accuracy did not improve from 0.61364

Epoch 140: val_accuracy did not improve from 0.61364

Epoch 141: val_accuracy did not improve from 0.61364

Epoch 142: val_accuracy did not improve from 0.61364

Epoch 143: val_accuracy did not improve from 0.61364

Epoch 144: val_accuracy did not improve from 0.61364

Epoch 145: val_accuracy did not improve from 0.61364

Epoch 146: val_accuracy did not improve from 0.61364

Epoch 147: val_accuracy did not improve from 0.61364

Epoch 148: val_accuracy did not improve from 0.61364

Epoch 149: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5

Epoch 150: val_accuracy did not improve from 0.63636

Epoch 151: val_accuracy did not improve from 0.63636

Epoch 152: val_accuracy did not improve from 0.63636

Epoch 153: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 154: val_accuracy did not improve from 0.65909

Epoch 155: val_accuracy did not improve from 0.65909

Epoch 156: val_accuracy did not improve from 0.65909

Epoch 157: val_accuracy did not improve from 0.65909

Epoch 158: val_accuracy did not improve from 0.65909

Epoch 159: val_accuracy did not improve from 0.65909

Epoch 160: val_accuracy did not improve from 0.65909

Epoch 161: val_accuracy did not improve from 0.65909

Epoch 162: val_accuracy did not improve from 0.65909

Epoch 163: val_accuracy did not improve from 0.65909

Epoch 164: val_accuracy did not improve from 0.65909

Epoch 165: val_accuracy did not improve from 0.65909

Epoch 166: val_accuracy did not improve from 0.65909

Epoch 167: val_accuracy did not improve from 0.65909

Epoch 168: val_accuracy did not improve from 0.65909

Epoch 169: val_accuracy did not improve from 0.65909

Epoch 170: val_accuracy did not improve from 0.65909

Epoch 171: val_accuracy did not improve from 0.65909

Epoch 172: val_accuracy did not improve from 0.65909

Epoch 173: val_accuracy did not improve from 0.65909
2/2 - 0s - loss: 0.6390 - accuracy: 0.6591 - 38ms/epoch - 19ms/step


#######################################################


the model mod130 use a learning rate = 7, l2 regularization = 4 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.70455

Epoch 3: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.70455

Epoch 5: val_accuracy did not improve from 0.70455

Epoch 6: val_accuracy did not improve from 0.70455

Epoch 7: val_accuracy did not improve from 0.70455

Epoch 8: val_accuracy did not improve from 0.70455

Epoch 9: val_accuracy did not improve from 0.70455

Epoch 10: val_accuracy did not improve from 0.70455

Epoch 11: val_accuracy did not improve from 0.70455

Epoch 12: val_accuracy did not improve from 0.70455

Epoch 13: val_accuracy did not improve from 0.70455

Epoch 14: val_accuracy did not improve from 0.70455

Epoch 15: val_accuracy did not improve from 0.70455

Epoch 16: val_accuracy did not improve from 0.70455

Epoch 17: val_accuracy did not improve from 0.70455

Epoch 18: val_accuracy did not improve from 0.70455

Epoch 19: val_accuracy did not improve from 0.70455

Epoch 20: val_accuracy did not improve from 0.70455

Epoch 21: val_accuracy did not improve from 0.70455
2/2 - 0s - loss: 0.7560 - accuracy: 0.7045 - 34ms/epoch - 17ms/step


#######################################################


the model mod131 use a learning rate = 8, l2 regularization = 4 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.65909, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.65909

Epoch 3: val_accuracy did not improve from 0.65909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.65909

Epoch 5: val_accuracy did not improve from 0.65909

Epoch 6: val_accuracy did not improve from 0.65909

Epoch 7: val_accuracy did not improve from 0.65909

Epoch 8: val_accuracy did not improve from 0.65909

Epoch 9: val_accuracy did not improve from 0.65909

Epoch 10: val_accuracy did not improve from 0.65909

Epoch 11: val_accuracy did not improve from 0.65909

Epoch 12: val_accuracy did not improve from 0.65909

Epoch 13: val_accuracy did not improve from 0.65909

Epoch 14: val_accuracy did not improve from 0.65909

Epoch 15: val_accuracy did not improve from 0.65909

Epoch 16: val_accuracy did not improve from 0.65909

Epoch 17: val_accuracy did not improve from 0.65909

Epoch 18: val_accuracy did not improve from 0.65909

Epoch 19: val_accuracy did not improve from 0.65909

Epoch 20: val_accuracy did not improve from 0.65909

Epoch 21: val_accuracy did not improve from 0.65909
2/2 - 0s - loss: 0.6901 - accuracy: 0.6591 - 74ms/epoch - 37ms/step


#######################################################


the model mod132 use a learning rate = 9, l2 regularization = 4 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.29545, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.29545

Epoch 3: val_accuracy did not improve from 0.29545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.29545

Epoch 5: val_accuracy did not improve from 0.29545

Epoch 6: val_accuracy did not improve from 0.29545

Epoch 7: val_accuracy did not improve from 0.29545

Epoch 8: val_accuracy did not improve from 0.29545

Epoch 9: val_accuracy did not improve from 0.29545

Epoch 10: val_accuracy did not improve from 0.29545

Epoch 11: val_accuracy did not improve from 0.29545

Epoch 12: val_accuracy did not improve from 0.29545

Epoch 13: val_accuracy did not improve from 0.29545

Epoch 14: val_accuracy did not improve from 0.29545

Epoch 15: val_accuracy did not improve from 0.29545

Epoch 16: val_accuracy did not improve from 0.29545

Epoch 17: val_accuracy did not improve from 0.29545

Epoch 18: val_accuracy did not improve from 0.29545

Epoch 19: val_accuracy did not improve from 0.29545

Epoch 20: val_accuracy did not improve from 0.29545

Epoch 21: val_accuracy did not improve from 0.29545
2/2 - 0s - loss: 1.0313 - accuracy: 0.2955 - 35ms/epoch - 17ms/step


#######################################################


the model mod133 use a learning rate = 0, l2 regularization = 4 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.88636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.88636

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 1.0391 - accuracy: 0.7273 - 36ms/epoch - 18ms/step


#######################################################


the model mod134 use a learning rate = 1, l2 regularization = 4 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.72727 to 0.81818, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.84091

Epoch 5: val_accuracy did not improve from 0.84091

Epoch 6: val_accuracy did not improve from 0.84091

Epoch 7: val_accuracy did not improve from 0.84091

Epoch 8: val_accuracy did not improve from 0.84091

Epoch 9: val_accuracy did not improve from 0.84091

Epoch 10: val_accuracy did not improve from 0.84091

Epoch 11: val_accuracy did not improve from 0.84091

Epoch 12: val_accuracy did not improve from 0.84091

Epoch 13: val_accuracy did not improve from 0.84091

Epoch 14: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy did not improve from 0.88636

Epoch 28: val_accuracy did not improve from 0.88636

Epoch 29: val_accuracy did not improve from 0.88636

Epoch 30: val_accuracy did not improve from 0.88636

Epoch 31: val_accuracy did not improve from 0.88636

Epoch 32: val_accuracy did not improve from 0.88636

Epoch 33: val_accuracy did not improve from 0.88636

Epoch 34: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.4260 - accuracy: 0.7955 - 44ms/epoch - 22ms/step


#######################################################


the model mod135 use a learning rate = 2, l2 regularization = 4 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.54545, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.54545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.54545

Epoch 4: val_accuracy did not improve from 0.54545

Epoch 5: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.56818

Epoch 7: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 8: val_accuracy improved from 0.59091 to 0.63636, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.63636

Epoch 10: val_accuracy improved from 0.63636 to 0.70455, saving model to best_model.h5

Epoch 11: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 12: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.75000

Epoch 14: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 15: val_accuracy improved from 0.77273 to 0.81818, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.81818

Epoch 17: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.84091

Epoch 19: val_accuracy did not improve from 0.84091

Epoch 20: val_accuracy did not improve from 0.84091

Epoch 21: val_accuracy did not improve from 0.84091

Epoch 22: val_accuracy did not improve from 0.84091

Epoch 23: val_accuracy did not improve from 0.84091

Epoch 24: val_accuracy did not improve from 0.84091

Epoch 25: val_accuracy did not improve from 0.84091

Epoch 26: val_accuracy did not improve from 0.84091

Epoch 27: val_accuracy did not improve from 0.84091

Epoch 28: val_accuracy did not improve from 0.84091

Epoch 29: val_accuracy did not improve from 0.84091

Epoch 30: val_accuracy did not improve from 0.84091

Epoch 31: val_accuracy did not improve from 0.84091

Epoch 32: val_accuracy did not improve from 0.84091

Epoch 33: val_accuracy did not improve from 0.84091

Epoch 34: val_accuracy did not improve from 0.84091

Epoch 35: val_accuracy did not improve from 0.84091

Epoch 36: val_accuracy did not improve from 0.84091

Epoch 37: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.3855 - accuracy: 0.8409 - 41ms/epoch - 20ms/step


#######################################################


the model mod136 use a learning rate = 3, l2 regularization = 4 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.38636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.38636

Epoch 3: val_accuracy did not improve from 0.38636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.38636

Epoch 5: val_accuracy did not improve from 0.38636

Epoch 6: val_accuracy did not improve from 0.38636

Epoch 7: val_accuracy did not improve from 0.38636

Epoch 8: val_accuracy did not improve from 0.38636

Epoch 9: val_accuracy did not improve from 0.38636

Epoch 10: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.40909

Epoch 12: val_accuracy did not improve from 0.40909

Epoch 13: val_accuracy did not improve from 0.40909

Epoch 14: val_accuracy did not improve from 0.40909

Epoch 15: val_accuracy did not improve from 0.40909

Epoch 16: val_accuracy did not improve from 0.40909

Epoch 17: val_accuracy did not improve from 0.40909

Epoch 18: val_accuracy did not improve from 0.40909

Epoch 19: val_accuracy did not improve from 0.40909

Epoch 20: val_accuracy did not improve from 0.40909

Epoch 21: val_accuracy did not improve from 0.40909

Epoch 22: val_accuracy did not improve from 0.40909

Epoch 23: val_accuracy did not improve from 0.40909

Epoch 24: val_accuracy did not improve from 0.40909

Epoch 25: val_accuracy did not improve from 0.40909

Epoch 26: val_accuracy did not improve from 0.40909

Epoch 27: val_accuracy did not improve from 0.40909

Epoch 28: val_accuracy did not improve from 0.40909

Epoch 29: val_accuracy did not improve from 0.40909

Epoch 30: val_accuracy did not improve from 0.40909
2/2 - 0s - loss: 0.9324 - accuracy: 0.4091 - 37ms/epoch - 18ms/step


#######################################################


the model mod137 use a learning rate = 4, l2 regularization = 4 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.70455
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.70455

Epoch 5: val_accuracy did not improve from 0.70455

Epoch 6: val_accuracy did not improve from 0.70455

Epoch 7: val_accuracy did not improve from 0.70455

Epoch 8: val_accuracy did not improve from 0.70455

Epoch 9: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.72727

Epoch 11: val_accuracy did not improve from 0.72727

Epoch 12: val_accuracy did not improve from 0.72727

Epoch 13: val_accuracy did not improve from 0.72727

Epoch 14: val_accuracy did not improve from 0.72727

Epoch 15: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.75000

Epoch 17: val_accuracy did not improve from 0.75000

Epoch 18: val_accuracy did not improve from 0.75000

Epoch 19: val_accuracy did not improve from 0.75000

Epoch 20: val_accuracy did not improve from 0.75000

Epoch 21: val_accuracy did not improve from 0.75000

Epoch 22: val_accuracy did not improve from 0.75000

Epoch 23: val_accuracy did not improve from 0.75000

Epoch 24: val_accuracy did not improve from 0.75000

Epoch 25: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 26: val_accuracy did not improve from 0.77273

Epoch 27: val_accuracy did not improve from 0.77273

Epoch 28: val_accuracy did not improve from 0.77273

Epoch 29: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 30: val_accuracy did not improve from 0.79545

Epoch 31: val_accuracy did not improve from 0.79545

Epoch 32: val_accuracy did not improve from 0.79545

Epoch 33: val_accuracy did not improve from 0.79545

Epoch 34: val_accuracy did not improve from 0.79545

Epoch 35: val_accuracy did not improve from 0.79545

Epoch 36: val_accuracy did not improve from 0.79545

Epoch 37: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 38: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 39: val_accuracy did not improve from 0.84091

Epoch 40: val_accuracy did not improve from 0.84091

Epoch 41: val_accuracy did not improve from 0.84091

Epoch 42: val_accuracy did not improve from 0.84091

Epoch 43: val_accuracy did not improve from 0.84091

Epoch 44: val_accuracy did not improve from 0.84091

Epoch 45: val_accuracy did not improve from 0.84091

Epoch 46: val_accuracy did not improve from 0.84091

Epoch 47: val_accuracy did not improve from 0.84091

Epoch 48: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 49: val_accuracy did not improve from 0.86364

Epoch 50: val_accuracy did not improve from 0.86364

Epoch 51: val_accuracy did not improve from 0.86364

Epoch 52: val_accuracy did not improve from 0.86364

Epoch 53: val_accuracy did not improve from 0.86364

Epoch 54: val_accuracy did not improve from 0.86364

Epoch 55: val_accuracy did not improve from 0.86364

Epoch 56: val_accuracy did not improve from 0.86364

Epoch 57: val_accuracy did not improve from 0.86364

Epoch 58: val_accuracy did not improve from 0.86364

Epoch 59: val_accuracy did not improve from 0.86364

Epoch 60: val_accuracy did not improve from 0.86364

Epoch 61: val_accuracy did not improve from 0.86364

Epoch 62: val_accuracy did not improve from 0.86364

Epoch 63: val_accuracy did not improve from 0.86364

Epoch 64: val_accuracy did not improve from 0.86364

Epoch 65: val_accuracy did not improve from 0.86364

Epoch 66: val_accuracy did not improve from 0.86364

Epoch 67: val_accuracy did not improve from 0.86364

Epoch 68: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.4758 - accuracy: 0.8636 - 35ms/epoch - 17ms/step


#######################################################


the model mod138 use a learning rate = 5, l2 regularization = 4 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.34091, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.34091

Epoch 3: val_accuracy improved from 0.34091 to 0.36364, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.36364 to 0.38636, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.40909

Epoch 7: val_accuracy did not improve from 0.40909

Epoch 8: val_accuracy did not improve from 0.40909

Epoch 9: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5

Epoch 10: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5

Epoch 11: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5

Epoch 12: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.50000

Epoch 14: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5

Epoch 15: val_accuracy did not improve from 0.52273

Epoch 16: val_accuracy improved from 0.52273 to 0.54545, saving model to best_model.h5

Epoch 17: val_accuracy did not improve from 0.54545

Epoch 18: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5

Epoch 19: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 20: val_accuracy did not improve from 0.59091

Epoch 21: val_accuracy did not improve from 0.59091

Epoch 22: val_accuracy did not improve from 0.59091

Epoch 23: val_accuracy did not improve from 0.59091

Epoch 24: val_accuracy did not improve from 0.59091

Epoch 25: val_accuracy did not improve from 0.59091

Epoch 26: val_accuracy did not improve from 0.59091

Epoch 27: val_accuracy did not improve from 0.59091

Epoch 28: val_accuracy improved from 0.59091 to 0.61364, saving model to best_model.h5

Epoch 29: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5

Epoch 30: val_accuracy did not improve from 0.63636

Epoch 31: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 32: val_accuracy did not improve from 0.65909

Epoch 33: val_accuracy did not improve from 0.65909

Epoch 34: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5

Epoch 35: val_accuracy did not improve from 0.68182

Epoch 36: val_accuracy did not improve from 0.68182

Epoch 37: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 38: val_accuracy did not improve from 0.70455

Epoch 39: val_accuracy did not improve from 0.70455

Epoch 40: val_accuracy did not improve from 0.70455

Epoch 41: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 42: val_accuracy did not improve from 0.72727

Epoch 43: val_accuracy did not improve from 0.72727

Epoch 44: val_accuracy did not improve from 0.72727

Epoch 45: val_accuracy did not improve from 0.72727

Epoch 46: val_accuracy did not improve from 0.72727

Epoch 47: val_accuracy did not improve from 0.72727

Epoch 48: val_accuracy did not improve from 0.72727

Epoch 49: val_accuracy did not improve from 0.72727

Epoch 50: val_accuracy did not improve from 0.72727

Epoch 51: val_accuracy did not improve from 0.72727

Epoch 52: val_accuracy did not improve from 0.72727

Epoch 53: val_accuracy did not improve from 0.72727

Epoch 54: val_accuracy did not improve from 0.72727

Epoch 55: val_accuracy did not improve from 0.72727

Epoch 56: val_accuracy did not improve from 0.72727

Epoch 57: val_accuracy did not improve from 0.72727

Epoch 58: val_accuracy did not improve from 0.72727

Epoch 59: val_accuracy did not improve from 0.72727

Epoch 60: val_accuracy did not improve from 0.72727

Epoch 61: val_accuracy did not improve from 0.72727
2/2 - 0s - loss: 0.7003 - accuracy: 0.7273 - 55ms/epoch - 28ms/step


#######################################################


the model mod139 use a learning rate = 6, l2 regularization = 4 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.47727, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.47727

Epoch 3: val_accuracy did not improve from 0.47727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.47727

Epoch 5: val_accuracy did not improve from 0.47727

Epoch 6: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.50000

Epoch 8: val_accuracy did not improve from 0.50000

Epoch 9: val_accuracy did not improve from 0.50000

Epoch 10: val_accuracy did not improve from 0.50000

Epoch 11: val_accuracy did not improve from 0.50000

Epoch 12: val_accuracy did not improve from 0.50000

Epoch 13: val_accuracy did not improve from 0.50000

Epoch 14: val_accuracy did not improve from 0.50000

Epoch 15: val_accuracy did not improve from 0.50000

Epoch 16: val_accuracy did not improve from 0.50000

Epoch 17: val_accuracy did not improve from 0.50000

Epoch 18: val_accuracy did not improve from 0.50000

Epoch 19: val_accuracy did not improve from 0.50000

Epoch 20: val_accuracy did not improve from 0.50000

Epoch 21: val_accuracy did not improve from 0.50000

Epoch 22: val_accuracy did not improve from 0.50000

Epoch 23: val_accuracy did not improve from 0.50000

Epoch 24: val_accuracy did not improve from 0.50000

Epoch 25: val_accuracy did not improve from 0.50000

Epoch 26: val_accuracy did not improve from 0.50000
2/2 - 0s - loss: 0.9501 - accuracy: 0.5000 - 50ms/epoch - 25ms/step


#######################################################


the model mod140 use a learning rate = 7, l2 regularization = 4 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.36364

Epoch 3: val_accuracy did not improve from 0.36364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.36364

Epoch 5: val_accuracy did not improve from 0.36364

Epoch 6: val_accuracy did not improve from 0.36364

Epoch 7: val_accuracy did not improve from 0.36364

Epoch 8: val_accuracy did not improve from 0.36364

Epoch 9: val_accuracy did not improve from 0.36364

Epoch 10: val_accuracy did not improve from 0.36364

Epoch 11: val_accuracy did not improve from 0.36364

Epoch 12: val_accuracy did not improve from 0.36364

Epoch 13: val_accuracy did not improve from 0.36364

Epoch 14: val_accuracy did not improve from 0.36364

Epoch 15: val_accuracy did not improve from 0.36364

Epoch 16: val_accuracy did not improve from 0.36364

Epoch 17: val_accuracy did not improve from 0.36364

Epoch 18: val_accuracy did not improve from 0.36364

Epoch 19: val_accuracy did not improve from 0.36364

Epoch 20: val_accuracy did not improve from 0.36364

Epoch 21: val_accuracy did not improve from 0.36364
2/2 - 0s - loss: 0.8779 - accuracy: 0.3636 - 37ms/epoch - 19ms/step


#######################################################


the model mod141 use a learning rate = 8, l2 regularization = 4 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.52273, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.52273
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.52273

Epoch 4: val_accuracy did not improve from 0.52273

Epoch 5: val_accuracy did not improve from 0.52273

Epoch 6: val_accuracy did not improve from 0.52273

Epoch 7: val_accuracy did not improve from 0.52273

Epoch 8: val_accuracy did not improve from 0.52273

Epoch 9: val_accuracy did not improve from 0.52273

Epoch 10: val_accuracy did not improve from 0.52273

Epoch 11: val_accuracy did not improve from 0.52273

Epoch 12: val_accuracy did not improve from 0.52273

Epoch 13: val_accuracy did not improve from 0.52273

Epoch 14: val_accuracy did not improve from 0.52273

Epoch 15: val_accuracy did not improve from 0.52273

Epoch 16: val_accuracy did not improve from 0.52273

Epoch 17: val_accuracy did not improve from 0.52273

Epoch 18: val_accuracy did not improve from 0.52273

Epoch 19: val_accuracy did not improve from 0.52273

Epoch 20: val_accuracy did not improve from 0.52273

Epoch 21: val_accuracy did not improve from 0.52273
2/2 - 0s - loss: 0.9006 - accuracy: 0.5227 - 36ms/epoch - 18ms/step


#######################################################


the model mod142 use a learning rate = 9, l2 regularization = 4 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.36364

Epoch 3: val_accuracy did not improve from 0.36364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.36364

Epoch 5: val_accuracy did not improve from 0.36364

Epoch 6: val_accuracy did not improve from 0.36364

Epoch 7: val_accuracy did not improve from 0.36364

Epoch 8: val_accuracy did not improve from 0.36364

Epoch 9: val_accuracy did not improve from 0.36364

Epoch 10: val_accuracy did not improve from 0.36364

Epoch 11: val_accuracy did not improve from 0.36364

Epoch 12: val_accuracy did not improve from 0.36364

Epoch 13: val_accuracy did not improve from 0.36364

Epoch 14: val_accuracy did not improve from 0.36364

Epoch 15: val_accuracy did not improve from 0.36364

Epoch 16: val_accuracy did not improve from 0.36364

Epoch 17: val_accuracy did not improve from 0.36364

Epoch 18: val_accuracy did not improve from 0.36364

Epoch 19: val_accuracy did not improve from 0.36364

Epoch 20: val_accuracy did not improve from 0.36364

Epoch 21: val_accuracy did not improve from 0.36364
2/2 - 0s - loss: 0.9550 - accuracy: 0.3636 - 36ms/epoch - 18ms/step


#######################################################


the model mod143 use a learning rate = 0, l2 regularization = 4 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.84091

Epoch 3: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.84091

Epoch 5: val_accuracy did not improve from 0.84091

Epoch 6: val_accuracy did not improve from 0.84091

Epoch 7: val_accuracy did not improve from 0.84091

Epoch 8: val_accuracy did not improve from 0.84091

Epoch 9: val_accuracy did not improve from 0.84091

Epoch 10: val_accuracy did not improve from 0.84091

Epoch 11: val_accuracy did not improve from 0.84091

Epoch 12: val_accuracy did not improve from 0.84091

Epoch 13: val_accuracy did not improve from 0.84091

Epoch 14: val_accuracy did not improve from 0.84091

Epoch 15: val_accuracy did not improve from 0.84091

Epoch 16: val_accuracy did not improve from 0.84091

Epoch 17: val_accuracy did not improve from 0.84091

Epoch 18: val_accuracy did not improve from 0.84091

Epoch 19: val_accuracy did not improve from 0.84091

Epoch 20: val_accuracy did not improve from 0.84091

Epoch 21: val_accuracy did not improve from 0.84091
2/2 - 0s - loss: 0.6591 - accuracy: 0.7727 - 34ms/epoch - 17ms/step


#######################################################


the model mod144 use a learning rate = 1, l2 regularization = 4 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.79545

Epoch 4: val_accuracy improved from 0.79545 to 0.86364, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.86364

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364

Epoch 23: val_accuracy did not improve from 0.86364

Epoch 24: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.6948 - accuracy: 0.8182 - 34ms/epoch - 17ms/step


#######################################################


the model mod145 use a learning rate = 2, l2 regularization = 4 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5

Epoch 4: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy did not improve from 0.90909

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.7669 - accuracy: 0.7727 - 34ms/epoch - 17ms/step


#######################################################


the model mod146 use a learning rate = 3, l2 regularization = 4 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.72727 to 0.88636, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.6180 - accuracy: 0.7955 - 35ms/epoch - 17ms/step


#######################################################


the model mod147 use a learning rate = 4, l2 regularization = 4 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.79545 to 0.90909, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.90909
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.90909

Epoch 5: val_accuracy did not improve from 0.90909

Epoch 6: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.93182

Epoch 8: val_accuracy did not improve from 0.93182

Epoch 9: val_accuracy did not improve from 0.93182

Epoch 10: val_accuracy did not improve from 0.93182

Epoch 11: val_accuracy did not improve from 0.93182

Epoch 12: val_accuracy did not improve from 0.93182

Epoch 13: val_accuracy did not improve from 0.93182

Epoch 14: val_accuracy did not improve from 0.93182

Epoch 15: val_accuracy did not improve from 0.93182

Epoch 16: val_accuracy did not improve from 0.93182

Epoch 17: val_accuracy did not improve from 0.93182

Epoch 18: val_accuracy did not improve from 0.93182

Epoch 19: val_accuracy did not improve from 0.93182

Epoch 20: val_accuracy did not improve from 0.93182

Epoch 21: val_accuracy did not improve from 0.93182

Epoch 22: val_accuracy did not improve from 0.93182

Epoch 23: val_accuracy did not improve from 0.93182

Epoch 24: val_accuracy did not improve from 0.93182

Epoch 25: val_accuracy did not improve from 0.93182

Epoch 26: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.5641 - accuracy: 0.8636 - 48ms/epoch - 24ms/step


#######################################################


the model mod148 use a learning rate = 5, l2 regularization = 4 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.75000 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 4: val_accuracy did not improve from 0.86364

Epoch 5: val_accuracy did not improve from 0.86364

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364

Epoch 23: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.5773 - accuracy: 0.8182 - 39ms/epoch - 19ms/step


#######################################################


the model mod149 use a learning rate = 6, l2 regularization = 4 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.86364

Epoch 5: val_accuracy did not improve from 0.86364

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.4497 - accuracy: 0.8409 - 40ms/epoch - 20ms/step


#######################################################


the model mod150 use a learning rate = 7, l2 regularization = 4 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.72727 to 0.86364, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.86364

Epoch 5: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.6559 - accuracy: 0.7955 - 34ms/epoch - 17ms/step


#######################################################


the model mod151 use a learning rate = 8, l2 regularization = 4 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.79545
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.79545

Epoch 4: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5038 - accuracy: 0.8182 - 34ms/epoch - 17ms/step


#######################################################


the model mod152 use a learning rate = 9, l2 regularization = 4 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.68182 to 0.81818, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.81818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.81818

Epoch 5: val_accuracy improved from 0.81818 to 0.86364, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364

Epoch 23: val_accuracy did not improve from 0.86364

Epoch 24: val_accuracy did not improve from 0.86364

Epoch 25: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.5896 - accuracy: 0.8636 - 34ms/epoch - 17ms/step


#######################################################


the model mod153 use a learning rate = 0, l2 regularization = 5 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.81818, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.84091

Epoch 5: val_accuracy did not improve from 0.84091

Epoch 6: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.9983 - accuracy: 0.7955 - 40ms/epoch - 20ms/step


#######################################################


the model mod154 use a learning rate = 1, l2 regularization = 5 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.93182, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.93182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.93182

Epoch 4: val_accuracy did not improve from 0.93182

Epoch 5: val_accuracy did not improve from 0.93182

Epoch 6: val_accuracy did not improve from 0.93182

Epoch 7: val_accuracy did not improve from 0.93182

Epoch 8: val_accuracy did not improve from 0.93182

Epoch 9: val_accuracy did not improve from 0.93182

Epoch 10: val_accuracy did not improve from 0.93182

Epoch 11: val_accuracy did not improve from 0.93182

Epoch 12: val_accuracy did not improve from 0.93182

Epoch 13: val_accuracy did not improve from 0.93182

Epoch 14: val_accuracy did not improve from 0.93182

Epoch 15: val_accuracy did not improve from 0.93182

Epoch 16: val_accuracy did not improve from 0.93182

Epoch 17: val_accuracy did not improve from 0.93182

Epoch 18: val_accuracy did not improve from 0.93182

Epoch 19: val_accuracy did not improve from 0.93182

Epoch 20: val_accuracy did not improve from 0.93182

Epoch 21: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 1.1696 - accuracy: 0.8182 - 34ms/epoch - 17ms/step


#######################################################


the model mod155 use a learning rate = 2, l2 regularization = 5 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.68182 to 0.84091, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.84091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.90909

Epoch 8: val_accuracy did not improve from 0.90909

Epoch 9: val_accuracy did not improve from 0.90909

Epoch 10: val_accuracy did not improve from 0.90909

Epoch 11: val_accuracy did not improve from 0.90909

Epoch 12: val_accuracy did not improve from 0.90909

Epoch 13: val_accuracy did not improve from 0.90909

Epoch 14: val_accuracy did not improve from 0.90909

Epoch 15: val_accuracy did not improve from 0.90909

Epoch 16: val_accuracy did not improve from 0.90909

Epoch 17: val_accuracy did not improve from 0.90909

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3829 - accuracy: 0.8409 - 36ms/epoch - 18ms/step


#######################################################


the model mod156 use a learning rate = 3, l2 regularization = 5 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.59091

Epoch 3: val_accuracy did not improve from 0.59091
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.59091

Epoch 5: val_accuracy improved from 0.59091 to 0.65909, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.65909

Epoch 7: val_accuracy improved from 0.65909 to 0.70455, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.70455

Epoch 9: val_accuracy did not improve from 0.70455

Epoch 10: val_accuracy did not improve from 0.70455

Epoch 11: val_accuracy did not improve from 0.70455

Epoch 12: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.72727

Epoch 14: val_accuracy did not improve from 0.72727

Epoch 15: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.75000

Epoch 17: val_accuracy did not improve from 0.75000

Epoch 18: val_accuracy did not improve from 0.75000

Epoch 19: val_accuracy did not improve from 0.75000

Epoch 20: val_accuracy did not improve from 0.75000

Epoch 21: val_accuracy did not improve from 0.75000

Epoch 22: val_accuracy did not improve from 0.75000

Epoch 23: val_accuracy did not improve from 0.75000

Epoch 24: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 25: val_accuracy did not improve from 0.77273

Epoch 26: val_accuracy did not improve from 0.77273

Epoch 27: val_accuracy did not improve from 0.77273

Epoch 28: val_accuracy did not improve from 0.77273

Epoch 29: val_accuracy did not improve from 0.77273

Epoch 30: val_accuracy did not improve from 0.77273

Epoch 31: val_accuracy did not improve from 0.77273

Epoch 32: val_accuracy did not improve from 0.77273

Epoch 33: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 34: val_accuracy did not improve from 0.79545

Epoch 35: val_accuracy did not improve from 0.79545

Epoch 36: val_accuracy did not improve from 0.79545

Epoch 37: val_accuracy did not improve from 0.79545

Epoch 38: val_accuracy did not improve from 0.79545

Epoch 39: val_accuracy did not improve from 0.79545

Epoch 40: val_accuracy did not improve from 0.79545

Epoch 41: val_accuracy did not improve from 0.79545

Epoch 42: val_accuracy did not improve from 0.79545

Epoch 43: val_accuracy did not improve from 0.79545

Epoch 44: val_accuracy did not improve from 0.79545

Epoch 45: val_accuracy did not improve from 0.79545

Epoch 46: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 47: val_accuracy did not improve from 0.81818

Epoch 48: val_accuracy did not improve from 0.81818

Epoch 49: val_accuracy did not improve from 0.81818

Epoch 50: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 51: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 52: val_accuracy did not improve from 0.86364

Epoch 53: val_accuracy did not improve from 0.86364

Epoch 54: val_accuracy did not improve from 0.86364

Epoch 55: val_accuracy did not improve from 0.86364

Epoch 56: val_accuracy did not improve from 0.86364

Epoch 57: val_accuracy did not improve from 0.86364

Epoch 58: val_accuracy did not improve from 0.86364

Epoch 59: val_accuracy did not improve from 0.86364

Epoch 60: val_accuracy did not improve from 0.86364

Epoch 61: val_accuracy did not improve from 0.86364

Epoch 62: val_accuracy did not improve from 0.86364

Epoch 63: val_accuracy did not improve from 0.86364

Epoch 64: val_accuracy did not improve from 0.86364

Epoch 65: val_accuracy did not improve from 0.86364

Epoch 66: val_accuracy did not improve from 0.86364

Epoch 67: val_accuracy did not improve from 0.86364

Epoch 68: val_accuracy did not improve from 0.86364

Epoch 69: val_accuracy did not improve from 0.86364

Epoch 70: val_accuracy did not improve from 0.86364

Epoch 71: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.3465 - accuracy: 0.8636 - 35ms/epoch - 18ms/step


#######################################################


the model mod157 use a learning rate = 4, l2 regularization = 5 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.72727 to 0.77273, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.77273 to 0.81818, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.81818 to 0.86364, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.3189 - accuracy: 0.8864 - 39ms/epoch - 19ms/step


#######################################################


the model mod158 use a learning rate = 5, l2 regularization = 5 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.59091, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.59091 to 0.65909, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.65909 to 0.70455, saving model to best_model.h5

Epoch 4: val_accuracy improved from 0.70455 to 0.77273, saving model to best_model.h5

Epoch 5: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.79545 to 0.86364, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.90909

Epoch 19: val_accuracy did not improve from 0.90909

Epoch 20: val_accuracy did not improve from 0.90909

Epoch 21: val_accuracy did not improve from 0.90909

Epoch 22: val_accuracy did not improve from 0.90909

Epoch 23: val_accuracy did not improve from 0.90909

Epoch 24: val_accuracy did not improve from 0.90909

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909

Epoch 35: val_accuracy did not improve from 0.90909

Epoch 36: val_accuracy did not improve from 0.90909

Epoch 37: val_accuracy did not improve from 0.90909
2/2 - 0s - loss: 0.3080 - accuracy: 0.9091 - 35ms/epoch - 17ms/step


#######################################################


the model mod159 use a learning rate = 6, l2 regularization = 5 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.61364, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.61364

Epoch 3: val_accuracy did not improve from 0.61364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.61364

Epoch 5: val_accuracy did not improve from 0.61364

Epoch 6: val_accuracy did not improve from 0.61364

Epoch 7: val_accuracy did not improve from 0.61364

Epoch 8: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.63636

Epoch 10: val_accuracy did not improve from 0.63636

Epoch 11: val_accuracy did not improve from 0.63636

Epoch 12: val_accuracy did not improve from 0.63636

Epoch 13: val_accuracy did not improve from 0.63636

Epoch 14: val_accuracy did not improve from 0.63636

Epoch 15: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.65909

Epoch 17: val_accuracy did not improve from 0.65909

Epoch 18: val_accuracy did not improve from 0.65909

Epoch 19: val_accuracy did not improve from 0.65909

Epoch 20: val_accuracy did not improve from 0.65909

Epoch 21: val_accuracy did not improve from 0.65909

Epoch 22: val_accuracy did not improve from 0.65909

Epoch 23: val_accuracy did not improve from 0.65909

Epoch 24: val_accuracy did not improve from 0.65909

Epoch 25: val_accuracy did not improve from 0.65909

Epoch 26: val_accuracy did not improve from 0.65909

Epoch 27: val_accuracy did not improve from 0.65909

Epoch 28: val_accuracy did not improve from 0.65909

Epoch 29: val_accuracy did not improve from 0.65909

Epoch 30: val_accuracy did not improve from 0.65909

Epoch 31: val_accuracy did not improve from 0.65909

Epoch 32: val_accuracy did not improve from 0.65909

Epoch 33: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5

Epoch 34: val_accuracy did not improve from 0.68182

Epoch 35: val_accuracy did not improve from 0.68182

Epoch 36: val_accuracy did not improve from 0.68182

Epoch 37: val_accuracy did not improve from 0.68182

Epoch 38: val_accuracy did not improve from 0.68182

Epoch 39: val_accuracy did not improve from 0.68182

Epoch 40: val_accuracy did not improve from 0.68182

Epoch 41: val_accuracy did not improve from 0.68182

Epoch 42: val_accuracy did not improve from 0.68182

Epoch 43: val_accuracy did not improve from 0.68182

Epoch 44: val_accuracy did not improve from 0.68182

Epoch 45: val_accuracy did not improve from 0.68182

Epoch 46: val_accuracy did not improve from 0.68182

Epoch 47: val_accuracy did not improve from 0.68182

Epoch 48: val_accuracy did not improve from 0.68182

Epoch 49: val_accuracy did not improve from 0.68182

Epoch 50: val_accuracy did not improve from 0.68182

Epoch 51: val_accuracy did not improve from 0.68182

Epoch 52: val_accuracy did not improve from 0.68182

Epoch 53: val_accuracy did not improve from 0.68182
2/2 - 0s - loss: 0.6591 - accuracy: 0.6818 - 34ms/epoch - 17ms/step


#######################################################


the model mod160 use a learning rate = 7, l2 regularization = 5 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.38636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.38636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.38636

Epoch 4: val_accuracy did not improve from 0.38636

Epoch 5: val_accuracy did not improve from 0.38636

Epoch 6: val_accuracy did not improve from 0.38636

Epoch 7: val_accuracy did not improve from 0.38636

Epoch 8: val_accuracy did not improve from 0.38636

Epoch 9: val_accuracy did not improve from 0.38636

Epoch 10: val_accuracy did not improve from 0.38636

Epoch 11: val_accuracy did not improve from 0.38636

Epoch 12: val_accuracy did not improve from 0.38636

Epoch 13: val_accuracy did not improve from 0.38636

Epoch 14: val_accuracy did not improve from 0.38636

Epoch 15: val_accuracy did not improve from 0.38636

Epoch 16: val_accuracy did not improve from 0.38636

Epoch 17: val_accuracy did not improve from 0.38636

Epoch 18: val_accuracy did not improve from 0.38636

Epoch 19: val_accuracy did not improve from 0.38636

Epoch 20: val_accuracy did not improve from 0.38636

Epoch 21: val_accuracy did not improve from 0.38636
2/2 - 0s - loss: 1.1079 - accuracy: 0.3864 - 48ms/epoch - 24ms/step


#######################################################


the model mod161 use a learning rate = 8, l2 regularization = 5 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.68182

Epoch 3: val_accuracy did not improve from 0.68182
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.68182

Epoch 5: val_accuracy did not improve from 0.68182

Epoch 6: val_accuracy did not improve from 0.68182

Epoch 7: val_accuracy did not improve from 0.68182

Epoch 8: val_accuracy did not improve from 0.68182

Epoch 9: val_accuracy did not improve from 0.68182

Epoch 10: val_accuracy did not improve from 0.68182

Epoch 11: val_accuracy did not improve from 0.68182

Epoch 12: val_accuracy did not improve from 0.68182

Epoch 13: val_accuracy did not improve from 0.68182

Epoch 14: val_accuracy did not improve from 0.68182

Epoch 15: val_accuracy did not improve from 0.68182

Epoch 16: val_accuracy did not improve from 0.68182

Epoch 17: val_accuracy did not improve from 0.68182

Epoch 18: val_accuracy did not improve from 0.68182

Epoch 19: val_accuracy did not improve from 0.68182

Epoch 20: val_accuracy did not improve from 0.68182

Epoch 21: val_accuracy did not improve from 0.68182
2/2 - 0s - loss: 0.6617 - accuracy: 0.6818 - 34ms/epoch - 17ms/step


#######################################################


the model mod162 use a learning rate = 9, l2 regularization = 5 and the optimizer = adam :

Epoch 1: val_accuracy improved from -inf to 0.56818, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.56818

Epoch 3: val_accuracy did not improve from 0.56818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.56818

Epoch 5: val_accuracy did not improve from 0.56818

Epoch 6: val_accuracy did not improve from 0.56818

Epoch 7: val_accuracy did not improve from 0.56818

Epoch 8: val_accuracy did not improve from 0.56818

Epoch 9: val_accuracy did not improve from 0.56818

Epoch 10: val_accuracy did not improve from 0.56818

Epoch 11: val_accuracy did not improve from 0.56818

Epoch 12: val_accuracy did not improve from 0.56818

Epoch 13: val_accuracy did not improve from 0.56818

Epoch 14: val_accuracy did not improve from 0.56818

Epoch 15: val_accuracy did not improve from 0.56818

Epoch 16: val_accuracy did not improve from 0.56818

Epoch 17: val_accuracy did not improve from 0.56818

Epoch 18: val_accuracy did not improve from 0.56818

Epoch 19: val_accuracy did not improve from 0.56818

Epoch 20: val_accuracy did not improve from 0.56818

Epoch 21: val_accuracy did not improve from 0.56818
2/2 - 0s - loss: 0.8059 - accuracy: 0.5682 - 34ms/epoch - 17ms/step


#######################################################


the model mod163 use a learning rate = 0, l2 regularization = 5 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.88636, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.88636

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.9870 - accuracy: 0.8409 - 38ms/epoch - 19ms/step


#######################################################


the model mod164 use a learning rate = 1, l2 regularization = 5 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.72727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.72727

Epoch 4: val_accuracy did not improve from 0.72727

Epoch 5: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.75000

Epoch 7: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.77273

Epoch 9: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 10: val_accuracy did not improve from 0.79545

Epoch 11: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.81818

Epoch 13: val_accuracy did not improve from 0.81818

Epoch 14: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 15: val_accuracy did not improve from 0.84091

Epoch 16: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 17: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636

Epoch 25: val_accuracy did not improve from 0.88636

Epoch 26: val_accuracy did not improve from 0.88636

Epoch 27: val_accuracy did not improve from 0.88636

Epoch 28: val_accuracy did not improve from 0.88636

Epoch 29: val_accuracy did not improve from 0.88636

Epoch 30: val_accuracy did not improve from 0.88636

Epoch 31: val_accuracy did not improve from 0.88636

Epoch 32: val_accuracy did not improve from 0.88636

Epoch 33: val_accuracy did not improve from 0.88636

Epoch 34: val_accuracy did not improve from 0.88636

Epoch 35: val_accuracy did not improve from 0.88636

Epoch 36: val_accuracy did not improve from 0.88636

Epoch 37: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.3758 - accuracy: 0.8636 - 36ms/epoch - 18ms/step


#######################################################


the model mod165 use a learning rate = 2, l2 regularization = 5 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.22727, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.22727 to 0.29545, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.29545 to 0.36364, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.36364 to 0.38636, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.38636

Epoch 6: val_accuracy improved from 0.38636 to 0.45455, saving model to best_model.h5

Epoch 7: val_accuracy improved from 0.45455 to 0.50000, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.50000

Epoch 9: val_accuracy improved from 0.50000 to 0.54545, saving model to best_model.h5

Epoch 10: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5

Epoch 11: val_accuracy improved from 0.56818 to 0.61364, saving model to best_model.h5

Epoch 12: val_accuracy did not improve from 0.61364

Epoch 13: val_accuracy did not improve from 0.61364

Epoch 14: val_accuracy did not improve from 0.61364

Epoch 15: val_accuracy improved from 0.61364 to 0.63636, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.63636

Epoch 17: val_accuracy did not improve from 0.63636

Epoch 18: val_accuracy did not improve from 0.63636

Epoch 19: val_accuracy improved from 0.63636 to 0.68182, saving model to best_model.h5

Epoch 20: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 21: val_accuracy did not improve from 0.70455

Epoch 22: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 23: val_accuracy did not improve from 0.72727

Epoch 24: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 25: val_accuracy did not improve from 0.75000

Epoch 26: val_accuracy did not improve from 0.75000

Epoch 27: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 28: val_accuracy did not improve from 0.77273

Epoch 29: val_accuracy did not improve from 0.77273

Epoch 30: val_accuracy did not improve from 0.77273

Epoch 31: val_accuracy did not improve from 0.77273

Epoch 32: val_accuracy did not improve from 0.77273

Epoch 33: val_accuracy did not improve from 0.77273

Epoch 34: val_accuracy improved from 0.77273 to 0.79545, saving model to best_model.h5

Epoch 35: val_accuracy did not improve from 0.79545

Epoch 36: val_accuracy did not improve from 0.79545

Epoch 37: val_accuracy did not improve from 0.79545

Epoch 38: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 39: val_accuracy did not improve from 0.81818

Epoch 40: val_accuracy did not improve from 0.81818

Epoch 41: val_accuracy did not improve from 0.81818

Epoch 42: val_accuracy did not improve from 0.81818

Epoch 43: val_accuracy did not improve from 0.81818

Epoch 44: val_accuracy did not improve from 0.81818

Epoch 45: val_accuracy did not improve from 0.81818

Epoch 46: val_accuracy did not improve from 0.81818

Epoch 47: val_accuracy did not improve from 0.81818

Epoch 48: val_accuracy did not improve from 0.81818

Epoch 49: val_accuracy did not improve from 0.81818

Epoch 50: val_accuracy did not improve from 0.81818

Epoch 51: val_accuracy did not improve from 0.81818

Epoch 52: val_accuracy did not improve from 0.81818

Epoch 53: val_accuracy did not improve from 0.81818

Epoch 54: val_accuracy did not improve from 0.81818

Epoch 55: val_accuracy did not improve from 0.81818

Epoch 56: val_accuracy did not improve from 0.81818

Epoch 57: val_accuracy did not improve from 0.81818

Epoch 58: val_accuracy did not improve from 0.81818
2/2 - 0s - loss: 0.4457 - accuracy: 0.8182 - 48ms/epoch - 24ms/step


#######################################################


the model mod166 use a learning rate = 3, l2 regularization = 5 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.50000

Epoch 3: val_accuracy did not improve from 0.50000
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.50000

Epoch 5: val_accuracy did not improve from 0.50000

Epoch 6: val_accuracy did not improve from 0.50000

Epoch 7: val_accuracy did not improve from 0.50000

Epoch 8: val_accuracy did not improve from 0.50000

Epoch 9: val_accuracy did not improve from 0.50000

Epoch 10: val_accuracy did not improve from 0.50000

Epoch 11: val_accuracy did not improve from 0.50000

Epoch 12: val_accuracy did not improve from 0.50000

Epoch 13: val_accuracy did not improve from 0.50000

Epoch 14: val_accuracy did not improve from 0.50000

Epoch 15: val_accuracy did not improve from 0.50000

Epoch 16: val_accuracy did not improve from 0.50000

Epoch 17: val_accuracy did not improve from 0.50000

Epoch 18: val_accuracy did not improve from 0.50000

Epoch 19: val_accuracy did not improve from 0.50000

Epoch 20: val_accuracy did not improve from 0.50000

Epoch 21: val_accuracy did not improve from 0.50000
2/2 - 0s - loss: 0.9275 - accuracy: 0.5000 - 39ms/epoch - 19ms/step


#######################################################


the model mod167 use a learning rate = 4, l2 regularization = 5 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.36364 to 0.38636, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.38636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.38636

Epoch 5: val_accuracy did not improve from 0.38636

Epoch 6: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.40909

Epoch 8: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5

Epoch 9: val_accuracy did not improve from 0.43182

Epoch 10: val_accuracy did not improve from 0.43182

Epoch 11: val_accuracy did not improve from 0.43182

Epoch 12: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5

Epoch 13: val_accuracy improved from 0.45455 to 0.50000, saving model to best_model.h5

Epoch 14: val_accuracy did not improve from 0.50000

Epoch 15: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5

Epoch 16: val_accuracy did not improve from 0.52273

Epoch 17: val_accuracy improved from 0.52273 to 0.54545, saving model to best_model.h5

Epoch 18: val_accuracy did not improve from 0.54545

Epoch 19: val_accuracy did not improve from 0.54545

Epoch 20: val_accuracy improved from 0.54545 to 0.56818, saving model to best_model.h5

Epoch 21: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 22: val_accuracy improved from 0.59091 to 0.63636, saving model to best_model.h5

Epoch 23: val_accuracy improved from 0.63636 to 0.65909, saving model to best_model.h5

Epoch 24: val_accuracy did not improve from 0.65909

Epoch 25: val_accuracy improved from 0.65909 to 0.68182, saving model to best_model.h5

Epoch 26: val_accuracy did not improve from 0.68182

Epoch 27: val_accuracy did not improve from 0.68182

Epoch 28: val_accuracy did not improve from 0.68182

Epoch 29: val_accuracy did not improve from 0.68182

Epoch 30: val_accuracy did not improve from 0.68182

Epoch 31: val_accuracy did not improve from 0.68182

Epoch 32: val_accuracy did not improve from 0.68182

Epoch 33: val_accuracy did not improve from 0.68182

Epoch 34: val_accuracy improved from 0.68182 to 0.70455, saving model to best_model.h5

Epoch 35: val_accuracy did not improve from 0.70455

Epoch 36: val_accuracy improved from 0.70455 to 0.72727, saving model to best_model.h5

Epoch 37: val_accuracy improved from 0.72727 to 0.75000, saving model to best_model.h5

Epoch 38: val_accuracy did not improve from 0.75000

Epoch 39: val_accuracy did not improve from 0.75000

Epoch 40: val_accuracy did not improve from 0.75000

Epoch 41: val_accuracy did not improve from 0.75000

Epoch 42: val_accuracy did not improve from 0.75000

Epoch 43: val_accuracy did not improve from 0.75000

Epoch 44: val_accuracy did not improve from 0.75000

Epoch 45: val_accuracy did not improve from 0.75000

Epoch 46: val_accuracy did not improve from 0.75000

Epoch 47: val_accuracy did not improve from 0.75000

Epoch 48: val_accuracy did not improve from 0.75000

Epoch 49: val_accuracy did not improve from 0.75000

Epoch 50: val_accuracy did not improve from 0.75000

Epoch 51: val_accuracy did not improve from 0.75000

Epoch 52: val_accuracy improved from 0.75000 to 0.77273, saving model to best_model.h5

Epoch 53: val_accuracy did not improve from 0.77273

Epoch 54: val_accuracy did not improve from 0.77273

Epoch 55: val_accuracy did not improve from 0.77273

Epoch 56: val_accuracy did not improve from 0.77273

Epoch 57: val_accuracy did not improve from 0.77273

Epoch 58: val_accuracy did not improve from 0.77273

Epoch 59: val_accuracy did not improve from 0.77273

Epoch 60: val_accuracy did not improve from 0.77273

Epoch 61: val_accuracy did not improve from 0.77273

Epoch 62: val_accuracy did not improve from 0.77273

Epoch 63: val_accuracy did not improve from 0.77273

Epoch 64: val_accuracy did not improve from 0.77273

Epoch 65: val_accuracy did not improve from 0.77273

Epoch 66: val_accuracy did not improve from 0.77273

Epoch 67: val_accuracy did not improve from 0.77273

Epoch 68: val_accuracy did not improve from 0.77273

Epoch 69: val_accuracy did not improve from 0.77273

Epoch 70: val_accuracy did not improve from 0.77273

Epoch 71: val_accuracy did not improve from 0.77273

Epoch 72: val_accuracy did not improve from 0.77273
2/2 - 0s - loss: 0.5593 - accuracy: 0.7727 - 41ms/epoch - 20ms/step


#######################################################


the model mod168 use a learning rate = 5, l2 regularization = 5 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.34091, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.34091

Epoch 3: val_accuracy improved from 0.34091 to 0.38636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.38636

Epoch 5: val_accuracy improved from 0.38636 to 0.40909, saving model to best_model.h5

Epoch 6: val_accuracy did not improve from 0.40909

Epoch 7: val_accuracy did not improve from 0.40909

Epoch 8: val_accuracy did not improve from 0.40909

Epoch 9: val_accuracy did not improve from 0.40909

Epoch 10: val_accuracy improved from 0.40909 to 0.43182, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.43182

Epoch 12: val_accuracy did not improve from 0.43182

Epoch 13: val_accuracy did not improve from 0.43182

Epoch 14: val_accuracy did not improve from 0.43182

Epoch 15: val_accuracy did not improve from 0.43182

Epoch 16: val_accuracy did not improve from 0.43182

Epoch 17: val_accuracy did not improve from 0.43182

Epoch 18: val_accuracy did not improve from 0.43182

Epoch 19: val_accuracy did not improve from 0.43182

Epoch 20: val_accuracy improved from 0.43182 to 0.45455, saving model to best_model.h5

Epoch 21: val_accuracy did not improve from 0.45455

Epoch 22: val_accuracy did not improve from 0.45455

Epoch 23: val_accuracy did not improve from 0.45455

Epoch 24: val_accuracy did not improve from 0.45455

Epoch 25: val_accuracy did not improve from 0.45455

Epoch 26: val_accuracy did not improve from 0.45455

Epoch 27: val_accuracy did not improve from 0.45455

Epoch 28: val_accuracy did not improve from 0.45455

Epoch 29: val_accuracy did not improve from 0.45455

Epoch 30: val_accuracy did not improve from 0.45455

Epoch 31: val_accuracy did not improve from 0.45455

Epoch 32: val_accuracy did not improve from 0.45455

Epoch 33: val_accuracy did not improve from 0.45455

Epoch 34: val_accuracy improved from 0.45455 to 0.47727, saving model to best_model.h5

Epoch 35: val_accuracy improved from 0.47727 to 0.50000, saving model to best_model.h5

Epoch 36: val_accuracy did not improve from 0.50000

Epoch 37: val_accuracy did not improve from 0.50000

Epoch 38: val_accuracy did not improve from 0.50000

Epoch 39: val_accuracy did not improve from 0.50000

Epoch 40: val_accuracy did not improve from 0.50000

Epoch 41: val_accuracy did not improve from 0.50000

Epoch 42: val_accuracy did not improve from 0.50000

Epoch 43: val_accuracy did not improve from 0.50000

Epoch 44: val_accuracy did not improve from 0.50000

Epoch 45: val_accuracy did not improve from 0.50000

Epoch 46: val_accuracy did not improve from 0.50000

Epoch 47: val_accuracy did not improve from 0.50000

Epoch 48: val_accuracy did not improve from 0.50000

Epoch 49: val_accuracy did not improve from 0.50000

Epoch 50: val_accuracy did not improve from 0.50000

Epoch 51: val_accuracy improved from 0.50000 to 0.52273, saving model to best_model.h5

Epoch 52: val_accuracy did not improve from 0.52273

Epoch 53: val_accuracy improved from 0.52273 to 0.56818, saving model to best_model.h5

Epoch 54: val_accuracy did not improve from 0.56818

Epoch 55: val_accuracy did not improve from 0.56818

Epoch 56: val_accuracy did not improve from 0.56818

Epoch 57: val_accuracy did not improve from 0.56818

Epoch 58: val_accuracy did not improve from 0.56818

Epoch 59: val_accuracy did not improve from 0.56818

Epoch 60: val_accuracy did not improve from 0.56818

Epoch 61: val_accuracy did not improve from 0.56818

Epoch 62: val_accuracy did not improve from 0.56818

Epoch 63: val_accuracy did not improve from 0.56818

Epoch 64: val_accuracy improved from 0.56818 to 0.59091, saving model to best_model.h5

Epoch 65: val_accuracy did not improve from 0.59091

Epoch 66: val_accuracy did not improve from 0.59091

Epoch 67: val_accuracy did not improve from 0.59091

Epoch 68: val_accuracy did not improve from 0.59091

Epoch 69: val_accuracy did not improve from 0.59091

Epoch 70: val_accuracy did not improve from 0.59091

Epoch 71: val_accuracy did not improve from 0.59091

Epoch 72: val_accuracy did not improve from 0.59091

Epoch 73: val_accuracy did not improve from 0.59091

Epoch 74: val_accuracy did not improve from 0.59091

Epoch 75: val_accuracy did not improve from 0.59091

Epoch 76: val_accuracy did not improve from 0.59091

Epoch 77: val_accuracy did not improve from 0.59091

Epoch 78: val_accuracy did not improve from 0.59091

Epoch 79: val_accuracy did not improve from 0.59091

Epoch 80: val_accuracy did not improve from 0.59091

Epoch 81: val_accuracy did not improve from 0.59091

Epoch 82: val_accuracy did not improve from 0.59091

Epoch 83: val_accuracy did not improve from 0.59091

Epoch 84: val_accuracy did not improve from 0.59091
2/2 - 0s - loss: 0.7157 - accuracy: 0.5909 - 72ms/epoch - 36ms/step


#######################################################


the model mod169 use a learning rate = 6, l2 regularization = 5 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.72727

Epoch 3: val_accuracy did not improve from 0.72727
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.72727

Epoch 5: val_accuracy did not improve from 0.72727

Epoch 6: val_accuracy did not improve from 0.72727

Epoch 7: val_accuracy did not improve from 0.72727

Epoch 8: val_accuracy did not improve from 0.72727

Epoch 9: val_accuracy did not improve from 0.72727

Epoch 10: val_accuracy did not improve from 0.72727

Epoch 11: val_accuracy did not improve from 0.72727

Epoch 12: val_accuracy did not improve from 0.72727

Epoch 13: val_accuracy did not improve from 0.72727

Epoch 14: val_accuracy did not improve from 0.72727

Epoch 15: val_accuracy did not improve from 0.72727

Epoch 16: val_accuracy did not improve from 0.72727

Epoch 17: val_accuracy did not improve from 0.72727

Epoch 18: val_accuracy did not improve from 0.72727

Epoch 19: val_accuracy did not improve from 0.72727

Epoch 20: val_accuracy did not improve from 0.72727

Epoch 21: val_accuracy did not improve from 0.72727
2/2 - 0s - loss: 0.7427 - accuracy: 0.7273 - 35ms/epoch - 17ms/step


#######################################################


the model mod170 use a learning rate = 7, l2 regularization = 5 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.31818, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.31818

Epoch 3: val_accuracy did not improve from 0.31818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.31818

Epoch 5: val_accuracy did not improve from 0.31818

Epoch 6: val_accuracy did not improve from 0.31818

Epoch 7: val_accuracy did not improve from 0.31818

Epoch 8: val_accuracy did not improve from 0.31818

Epoch 9: val_accuracy did not improve from 0.31818

Epoch 10: val_accuracy did not improve from 0.31818

Epoch 11: val_accuracy did not improve from 0.31818

Epoch 12: val_accuracy did not improve from 0.31818

Epoch 13: val_accuracy did not improve from 0.31818

Epoch 14: val_accuracy did not improve from 0.31818

Epoch 15: val_accuracy did not improve from 0.31818

Epoch 16: val_accuracy did not improve from 0.31818

Epoch 17: val_accuracy did not improve from 0.31818

Epoch 18: val_accuracy did not improve from 0.31818

Epoch 19: val_accuracy did not improve from 0.31818

Epoch 20: val_accuracy did not improve from 0.31818

Epoch 21: val_accuracy did not improve from 0.31818
2/2 - 0s - loss: 0.9423 - accuracy: 0.3182 - 36ms/epoch - 18ms/step


#######################################################


the model mod171 use a learning rate = 8, l2 regularization = 5 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.36364, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.36364

Epoch 3: val_accuracy did not improve from 0.36364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.36364

Epoch 5: val_accuracy did not improve from 0.36364

Epoch 6: val_accuracy did not improve from 0.36364

Epoch 7: val_accuracy did not improve from 0.36364

Epoch 8: val_accuracy did not improve from 0.36364

Epoch 9: val_accuracy did not improve from 0.36364

Epoch 10: val_accuracy did not improve from 0.36364

Epoch 11: val_accuracy did not improve from 0.36364

Epoch 12: val_accuracy did not improve from 0.36364

Epoch 13: val_accuracy did not improve from 0.36364

Epoch 14: val_accuracy did not improve from 0.36364

Epoch 15: val_accuracy did not improve from 0.36364

Epoch 16: val_accuracy did not improve from 0.36364

Epoch 17: val_accuracy did not improve from 0.36364

Epoch 18: val_accuracy did not improve from 0.36364

Epoch 19: val_accuracy did not improve from 0.36364

Epoch 20: val_accuracy did not improve from 0.36364

Epoch 21: val_accuracy did not improve from 0.36364
2/2 - 0s - loss: 0.8387 - accuracy: 0.3636 - 39ms/epoch - 20ms/step


#######################################################


the model mod172 use a learning rate = 9, l2 regularization = 5 and the optimizer = adagrad :

Epoch 1: val_accuracy improved from -inf to 0.61364, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.61364

Epoch 3: val_accuracy did not improve from 0.61364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.61364

Epoch 5: val_accuracy did not improve from 0.61364

Epoch 6: val_accuracy did not improve from 0.61364

Epoch 7: val_accuracy did not improve from 0.61364

Epoch 8: val_accuracy did not improve from 0.61364

Epoch 9: val_accuracy did not improve from 0.61364

Epoch 10: val_accuracy did not improve from 0.61364

Epoch 11: val_accuracy did not improve from 0.61364

Epoch 12: val_accuracy did not improve from 0.61364

Epoch 13: val_accuracy did not improve from 0.61364

Epoch 14: val_accuracy did not improve from 0.61364

Epoch 15: val_accuracy did not improve from 0.61364

Epoch 16: val_accuracy did not improve from 0.61364

Epoch 17: val_accuracy did not improve from 0.61364

Epoch 18: val_accuracy did not improve from 0.61364

Epoch 19: val_accuracy did not improve from 0.61364

Epoch 20: val_accuracy did not improve from 0.61364

Epoch 21: val_accuracy did not improve from 0.61364
2/2 - 0s - loss: 0.6587 - accuracy: 0.6136 - 63ms/epoch - 32ms/step


#######################################################


the model mod173 use a learning rate = 0, l2 regularization = 5 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5719 - accuracy: 0.8182 - 40ms/epoch - 20ms/step


#######################################################


the model mod174 use a learning rate = 1, l2 regularization = 5 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.84091, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.5637 - accuracy: 0.8409 - 34ms/epoch - 17ms/step


#######################################################


the model mod175 use a learning rate = 2, l2 regularization = 5 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.75000 to 0.84091, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 4: val_accuracy did not improve from 0.86364

Epoch 5: val_accuracy did not improve from 0.86364

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364

Epoch 23: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.5411 - accuracy: 0.8182 - 48ms/epoch - 24ms/step


#######################################################


the model mod176 use a learning rate = 3, l2 regularization = 5 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.77273 to 0.86364, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy improved from 0.86364 to 0.90909, saving model to best_model.h5

Epoch 4: val_accuracy improved from 0.90909 to 0.93182, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.93182

Epoch 6: val_accuracy did not improve from 0.93182

Epoch 7: val_accuracy did not improve from 0.93182

Epoch 8: val_accuracy did not improve from 0.93182

Epoch 9: val_accuracy did not improve from 0.93182

Epoch 10: val_accuracy did not improve from 0.93182

Epoch 11: val_accuracy did not improve from 0.93182

Epoch 12: val_accuracy did not improve from 0.93182

Epoch 13: val_accuracy did not improve from 0.93182

Epoch 14: val_accuracy did not improve from 0.93182

Epoch 15: val_accuracy did not improve from 0.93182

Epoch 16: val_accuracy did not improve from 0.93182

Epoch 17: val_accuracy did not improve from 0.93182

Epoch 18: val_accuracy did not improve from 0.93182

Epoch 19: val_accuracy did not improve from 0.93182

Epoch 20: val_accuracy did not improve from 0.93182

Epoch 21: val_accuracy did not improve from 0.93182

Epoch 22: val_accuracy did not improve from 0.93182

Epoch 23: val_accuracy did not improve from 0.93182

Epoch 24: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.5174 - accuracy: 0.8409 - 39ms/epoch - 19ms/step


#######################################################


the model mod177 use a learning rate = 4, l2 regularization = 5 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.88636
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.88636

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy improved from 0.88636 to 0.93182, saving model to best_model.h5

Epoch 8: val_accuracy did not improve from 0.93182

Epoch 9: val_accuracy did not improve from 0.93182

Epoch 10: val_accuracy did not improve from 0.93182

Epoch 11: val_accuracy did not improve from 0.93182

Epoch 12: val_accuracy did not improve from 0.93182

Epoch 13: val_accuracy did not improve from 0.93182

Epoch 14: val_accuracy did not improve from 0.93182

Epoch 15: val_accuracy did not improve from 0.93182

Epoch 16: val_accuracy did not improve from 0.93182

Epoch 17: val_accuracy did not improve from 0.93182

Epoch 18: val_accuracy did not improve from 0.93182

Epoch 19: val_accuracy did not improve from 0.93182

Epoch 20: val_accuracy did not improve from 0.93182

Epoch 21: val_accuracy did not improve from 0.93182

Epoch 22: val_accuracy did not improve from 0.93182

Epoch 23: val_accuracy did not improve from 0.93182

Epoch 24: val_accuracy did not improve from 0.93182

Epoch 25: val_accuracy did not improve from 0.93182

Epoch 26: val_accuracy did not improve from 0.93182

Epoch 27: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.5171 - accuracy: 0.8409 - 34ms/epoch - 17ms/step


#######################################################


the model mod178 use a learning rate = 5, l2 regularization = 5 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.75000 to 0.86364, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.86364 to 0.88636, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.88636

Epoch 6: val_accuracy did not improve from 0.88636

Epoch 7: val_accuracy did not improve from 0.88636

Epoch 8: val_accuracy did not improve from 0.88636

Epoch 9: val_accuracy did not improve from 0.88636

Epoch 10: val_accuracy did not improve from 0.88636

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy did not improve from 0.88636
2/2 - 0s - loss: 0.6274 - accuracy: 0.7955 - 66ms/epoch - 33ms/step


#######################################################


the model mod179 use a learning rate = 6, l2 regularization = 5 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.70455, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.70455 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 3: val_accuracy did not improve from 0.79545

Epoch 4: val_accuracy improved from 0.79545 to 0.81818, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.81818

Epoch 6: val_accuracy did not improve from 0.81818

Epoch 7: val_accuracy did not improve from 0.81818

Epoch 8: val_accuracy did not improve from 0.81818

Epoch 9: val_accuracy did not improve from 0.81818

Epoch 10: val_accuracy did not improve from 0.81818

Epoch 11: val_accuracy did not improve from 0.81818

Epoch 12: val_accuracy improved from 0.81818 to 0.86364, saving model to best_model.h5

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364

Epoch 23: val_accuracy did not improve from 0.86364

Epoch 24: val_accuracy did not improve from 0.86364

Epoch 25: val_accuracy did not improve from 0.86364

Epoch 26: val_accuracy did not improve from 0.86364

Epoch 27: val_accuracy did not improve from 0.86364

Epoch 28: val_accuracy did not improve from 0.86364

Epoch 29: val_accuracy did not improve from 0.86364

Epoch 30: val_accuracy did not improve from 0.86364

Epoch 31: val_accuracy did not improve from 0.86364

Epoch 32: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.5747 - accuracy: 0.7727 - 44ms/epoch - 22ms/step


#######################################################


the model mod180 use a learning rate = 7, l2 regularization = 5 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.79545, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.79545 to 0.86364, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.86364
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.86364

Epoch 5: val_accuracy did not improve from 0.86364

Epoch 6: val_accuracy did not improve from 0.86364

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.5834 - accuracy: 0.8182 - 40ms/epoch - 20ms/step


#######################################################


the model mod181 use a learning rate = 8, l2 regularization = 5 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.72727, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.72727 to 0.81818, saving model to best_model.h5

Epoch 3: val_accuracy did not improve from 0.81818
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy did not improve from 0.81818

Epoch 5: val_accuracy improved from 0.81818 to 0.84091, saving model to best_model.h5

Epoch 6: val_accuracy improved from 0.84091 to 0.86364, saving model to best_model.h5

Epoch 7: val_accuracy did not improve from 0.86364

Epoch 8: val_accuracy did not improve from 0.86364

Epoch 9: val_accuracy did not improve from 0.86364

Epoch 10: val_accuracy did not improve from 0.86364

Epoch 11: val_accuracy did not improve from 0.86364

Epoch 12: val_accuracy did not improve from 0.86364

Epoch 13: val_accuracy did not improve from 0.86364

Epoch 14: val_accuracy did not improve from 0.86364

Epoch 15: val_accuracy did not improve from 0.86364

Epoch 16: val_accuracy did not improve from 0.86364

Epoch 17: val_accuracy did not improve from 0.86364

Epoch 18: val_accuracy did not improve from 0.86364

Epoch 19: val_accuracy did not improve from 0.86364

Epoch 20: val_accuracy did not improve from 0.86364

Epoch 21: val_accuracy did not improve from 0.86364

Epoch 22: val_accuracy did not improve from 0.86364

Epoch 23: val_accuracy did not improve from 0.86364

Epoch 24: val_accuracy did not improve from 0.86364

Epoch 25: val_accuracy did not improve from 0.86364

Epoch 26: val_accuracy did not improve from 0.86364
2/2 - 0s - loss: 0.7406 - accuracy: 0.7727 - 43ms/epoch - 21ms/step


#######################################################


the model mod182 use a learning rate = 9, l2 regularization = 5 and the optimizer = SGD :

Epoch 1: val_accuracy improved from -inf to 0.77273, saving model to best_model.h5

Epoch 2: val_accuracy improved from 0.77273 to 0.84091, saving model to best_model.h5

Epoch 3: val_accuracy improved from 0.84091 to 0.88636, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 4: val_accuracy improved from 0.88636 to 0.93182, saving model to best_model.h5

Epoch 5: val_accuracy did not improve from 0.93182

Epoch 6: val_accuracy did not improve from 0.93182

Epoch 7: val_accuracy did not improve from 0.93182

Epoch 8: val_accuracy did not improve from 0.93182

Epoch 9: val_accuracy did not improve from 0.93182

Epoch 10: val_accuracy did not improve from 0.93182

Epoch 11: val_accuracy did not improve from 0.93182

Epoch 12: val_accuracy did not improve from 0.93182

Epoch 13: val_accuracy did not improve from 0.93182

Epoch 14: val_accuracy did not improve from 0.93182

Epoch 15: val_accuracy did not improve from 0.93182

Epoch 16: val_accuracy did not improve from 0.93182

Epoch 17: val_accuracy did not improve from 0.93182

Epoch 18: val_accuracy did not improve from 0.93182

Epoch 19: val_accuracy did not improve from 0.93182

Epoch 20: val_accuracy did not improve from 0.93182

Epoch 21: val_accuracy did not improve from 0.93182

Epoch 22: val_accuracy did not improve from 0.93182

Epoch 23: val_accuracy did not improve from 0.93182

Epoch 24: val_accuracy did not improve from 0.93182
2/2 - 0s - loss: 0.5378 - accuracy: 0.8636 - 37ms/epoch - 18ms/step
In [ ]:
classement=sorted(classement, reverse = True)
 print(str(i+1)+"-" + str(classement[i][1])+" with an accuracy of :"+ str(round(classement[i][0]*100,3)))
1-mod82 with an accuracy of :90.909
2-mod60 with an accuracy of :90.909
3-mod5 with an accuracy of :90.909
4-mod28 with an accuracy of :90.909
5-mod158 with an accuracy of :90.909
6-mod104 with an accuracy of :90.909
7-mod97 with an accuracy of :88.636
8-mod87 with an accuracy of :88.636
9-mod84 with an accuracy of :88.636
10-mod8 with an accuracy of :88.636
11-mod67 with an accuracy of :88.636
12-mod56 with an accuracy of :88.636
13-mod55 with an accuracy of :88.636
14-mod44 with an accuracy of :88.636
15-mod43 with an accuracy of :88.636
16-mod38 with an accuracy of :88.636
17-mod37 with an accuracy of :88.636
18-mod36 with an accuracy of :88.636
19-mod35 with an accuracy of :88.636
20-mod25 with an accuracy of :88.636
21-mod157 with an accuracy of :88.636
22-mod14 with an accuracy of :88.636
23-mod127 with an accuracy of :88.636
24-mod119 with an accuracy of :88.636
25-mod92 with an accuracy of :86.364
26-mod90 with an accuracy of :86.364
27-mod68 with an accuracy of :86.364
28-mod65 with an accuracy of :86.364
29-mod53 with an accuracy of :86.364
30-mod45 with an accuracy of :86.364
31-mod4 with an accuracy of :86.364
32-mod26 with an accuracy of :86.364
33-mod182 with an accuracy of :86.364
34-mod164 with an accuracy of :86.364
35-mod156 with an accuracy of :86.364
36-mod152 with an accuracy of :86.364
37-mod15 with an accuracy of :86.364
38-mod147 with an accuracy of :86.364
39-mod137 with an accuracy of :86.364
40-mod126 with an accuracy of :86.364
41-mod125 with an accuracy of :86.364
42-mod120 with an accuracy of :86.364
43-mod96 with an accuracy of :84.091
44-mod95 with an accuracy of :84.091
45-mod91 with an accuracy of :84.091
46-mod88 with an accuracy of :84.091
47-mod85 with an accuracy of :84.091
48-mod83 with an accuracy of :84.091
49-mod79 with an accuracy of :84.091
50-mod74 with an accuracy of :84.091
51-mod62 with an accuracy of :84.091
52-mod57 with an accuracy of :84.091
53-mod31 with an accuracy of :84.091
54-mod177 with an accuracy of :84.091
55-mod176 with an accuracy of :84.091
56-mod174 with an accuracy of :84.091
57-mod163 with an accuracy of :84.091
58-mod155 with an accuracy of :84.091
59-mod149 with an accuracy of :84.091
60-mod135 with an accuracy of :84.091
61-mod128 with an accuracy of :84.091
62-mod118 with an accuracy of :84.091
63-mod98 with an accuracy of :81.818
64-mod94 with an accuracy of :81.818
65-mod89 with an accuracy of :81.818
66-mod86 with an accuracy of :81.818
67-mod75 with an accuracy of :81.818
68-mod64 with an accuracy of :81.818
69-mod61 with an accuracy of :81.818
70-mod59 with an accuracy of :81.818
71-mod58 with an accuracy of :81.818
72-mod54 with an accuracy of :81.818
73-mod34 with an accuracy of :81.818
74-mod32 with an accuracy of :81.818
75-mod30 with an accuracy of :81.818
76-mod27 with an accuracy of :81.818
77-mod24 with an accuracy of :81.818
78-mod180 with an accuracy of :81.818
79-mod175 with an accuracy of :81.818
80-mod173 with an accuracy of :81.818
81-mod17 with an accuracy of :81.818
82-mod165 with an accuracy of :81.818
83-mod154 with an accuracy of :81.818
84-mod151 with an accuracy of :81.818
85-mod148 with an accuracy of :81.818
86-mod144 with an accuracy of :81.818
87-mod116 with an accuracy of :81.818
88-mod115 with an accuracy of :81.818
89-mod114 with an accuracy of :81.818
90-mod77 with an accuracy of :79.545
91-mod73 with an accuracy of :79.545
92-mod66 with an accuracy of :79.545
93-mod6 with an accuracy of :79.545
94-mod29 with an accuracy of :79.545
95-mod178 with an accuracy of :79.545
96-mod153 with an accuracy of :79.545
97-mod150 with an accuracy of :79.545
98-mod146 with an accuracy of :79.545
99-mod134 with an accuracy of :79.545
100-mod124 with an accuracy of :79.545
101-mod122 with an accuracy of :79.545
102-mod113 with an accuracy of :79.545
103-mod105 with an accuracy of :79.545
104-mod78 with an accuracy of :77.273
105-mod72 with an accuracy of :77.273
106-mod7 with an accuracy of :77.273
107-mod181 with an accuracy of :77.273
108-mod179 with an accuracy of :77.273
109-mod167 with an accuracy of :77.273
110-mod145 with an accuracy of :77.273
111-mod143 with an accuracy of :77.273
112-mod117 with an accuracy of :77.273
113-mod107 with an accuracy of :77.273
114-mod70 with an accuracy of :75.0
115-mod51 with an accuracy of :75.0
116-mod121 with an accuracy of :75.0
117-mod108 with an accuracy of :75.0
118-mod106 with an accuracy of :75.0
119-mod103 with an accuracy of :75.0
120-mod102 with an accuracy of :75.0
121-mod9 with an accuracy of :72.727
122-mod169 with an accuracy of :72.727
123-mod138 with an accuracy of :72.727
124-mod133 with an accuracy of :72.727
125-mod47 with an accuracy of :70.455
126-mod130 with an accuracy of :70.455
127-mod10 with an accuracy of :70.455
128-mod48 with an accuracy of :68.182
129-mod161 with an accuracy of :68.182
130-mod16 with an accuracy of :68.182
131-mod159 with an accuracy of :68.182
132-mod123 with an accuracy of :68.182
133-mod11 with an accuracy of :68.182
134-mod69 with an accuracy of :65.909
135-mod131 with an accuracy of :65.909
136-mod129 with an accuracy of :65.909
137-mod101 with an accuracy of :65.909
138-mod23 with an accuracy of :63.636
139-mod112 with an accuracy of :63.636
140-mod93 with an accuracy of :61.364
141-mod49 with an accuracy of :61.364
142-mod172 with an accuracy of :61.364
143-mod76 with an accuracy of :59.091
144-mod168 with an accuracy of :59.091
145-mod100 with an accuracy of :59.091
146-mod20 with an accuracy of :56.818
147-mod162 with an accuracy of :56.818
148-mod80 with an accuracy of :54.545
149-mod63 with an accuracy of :54.545
150-mod46 with an accuracy of :54.545
151-mod33 with an accuracy of :54.545
152-mod3 with an accuracy of :54.545
153-mod22 with an accuracy of :54.545
154-mod19 with an accuracy of :54.545
155-mod41 with an accuracy of :52.273
156-mod141 with an accuracy of :52.273
157-mod52 with an accuracy of :50.0
158-mod18 with an accuracy of :50.0
159-mod166 with an accuracy of :50.0
160-mod139 with an accuracy of :50.0
161-mod13 with an accuracy of :50.0
162-mod81 with an accuracy of :47.727
163-mod12 with an accuracy of :47.727
164-mod111 with an accuracy of :45.455
165-mod99 with an accuracy of :43.182
166-mod40 with an accuracy of :40.909
167-mod136 with an accuracy of :40.909
168-mod109 with an accuracy of :40.909
169-mod160 with an accuracy of :38.636
170-mod21 with an accuracy of :36.364
171-mod171 with an accuracy of :36.364
172-mod142 with an accuracy of :36.364
173-mod140 with an accuracy of :36.364
174-mod110 with an accuracy of :34.091
175-mod50 with an accuracy of :31.818
176-mod170 with an accuracy of :31.818
177-mod132 with an accuracy of :29.545
178-mod71 with an accuracy of :27.273
179-mod42 with an accuracy of :22.727
180-mod39 with an accuracy of :18.182

Now that we tested all the possible models, we will select the most performant model that doesn't overfit.

When we look at the learning curve

  1. mod82 with an accuracy of :90.909 => overfit a lot
  2. mod60 with an accuracy of :90.909 => overfit a little
  3. mod5 with an accuracy of :90.909 => overfit a little
  4. mod28 with an accuracy of :90.909 => overfit a little
  5. mod158 with an accuracy of :90.909 => overfit a lot
  6. mod104 with an accuracy of :90.909 => doesn't overfit / overfit a little
  7. mod97 with an accuracy of :88.636 => doesn't overfit / overfit a little
  8. mod87 with an accuracy of :88.636 => doesn't overfit but converges with difficulty
  9. mod84 with an accuracy of :88.636 => doesn't overfit but converges with difficulty
  10. mod8 with an accuracy of :88.636 => doesn't overfit / overfit a little

with this analysis we will use the model mod104 because the curves of the learning rate of the train and the test are closer in general and the performance are better.

Validation and final prediction:¶

In [13]:
model = keras.Sequential([
    keras.layers.Dense(13, input_dim=13, activation='relu', kernel_initializer='he_uniform',kernel_regularizer=keras.regularizers.l2(3)),
    #13 neurons because it's the number of columns/inputs
    keras.layers.Dense(2, activation='sigmoid')
    #2 neurons because we want a classification with two labels
])
es = EarlyStopping(monitor='val_accuracy', mode='auto', verbose=0,patience=20)
mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)
opt=Adagrad(learning_rate= 1)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=1800, verbose=0, callbacks=[es, mc])
Epoch 1: val_accuracy improved from -inf to 0.68182, saving model to best_model.h5

Epoch 2: val_accuracy did not improve from 0.68182

Epoch 3: val_accuracy did not improve from 0.68182

Epoch 4: val_accuracy improved from 0.68182 to 0.79545, saving model to best_model.h5
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
Epoch 5: val_accuracy did not improve from 0.79545

Epoch 6: val_accuracy did not improve from 0.79545

Epoch 7: val_accuracy did not improve from 0.79545

Epoch 8: val_accuracy did not improve from 0.79545

Epoch 9: val_accuracy did not improve from 0.79545

Epoch 10: val_accuracy improved from 0.79545 to 0.88636, saving model to best_model.h5

Epoch 11: val_accuracy did not improve from 0.88636

Epoch 12: val_accuracy did not improve from 0.88636

Epoch 13: val_accuracy did not improve from 0.88636

Epoch 14: val_accuracy did not improve from 0.88636

Epoch 15: val_accuracy did not improve from 0.88636

Epoch 16: val_accuracy did not improve from 0.88636

Epoch 17: val_accuracy did not improve from 0.88636

Epoch 18: val_accuracy did not improve from 0.88636

Epoch 19: val_accuracy did not improve from 0.88636

Epoch 20: val_accuracy did not improve from 0.88636

Epoch 21: val_accuracy did not improve from 0.88636

Epoch 22: val_accuracy did not improve from 0.88636

Epoch 23: val_accuracy did not improve from 0.88636

Epoch 24: val_accuracy improved from 0.88636 to 0.90909, saving model to best_model.h5

Epoch 25: val_accuracy did not improve from 0.90909

Epoch 26: val_accuracy did not improve from 0.90909

Epoch 27: val_accuracy did not improve from 0.90909

Epoch 28: val_accuracy did not improve from 0.90909

Epoch 29: val_accuracy did not improve from 0.90909

Epoch 30: val_accuracy did not improve from 0.90909

Epoch 31: val_accuracy did not improve from 0.90909

Epoch 32: val_accuracy did not improve from 0.90909

Epoch 33: val_accuracy did not improve from 0.90909

Epoch 34: val_accuracy did not improve from 0.90909

Epoch 35: val_accuracy did not improve from 0.90909

Epoch 36: val_accuracy did not improve from 0.90909

Epoch 37: val_accuracy did not improve from 0.90909

Epoch 38: val_accuracy did not improve from 0.90909

Epoch 39: val_accuracy did not improve from 0.90909

Epoch 40: val_accuracy did not improve from 0.90909

Epoch 41: val_accuracy did not improve from 0.90909

Epoch 42: val_accuracy did not improve from 0.90909

Epoch 43: val_accuracy did not improve from 0.90909

Epoch 44: val_accuracy did not improve from 0.90909
In [23]:
X = small_validation.drop(columns="disease")
y= small_validation["disease"]
X-= mean
X /= std
small_predictions = model.predict(X)
result =[]
for i in range(len(small_predictions)):
  if small_predictions[i][0]>small_predictions[i][1]:
    result.append(1)#the real class
  else :
    result.append(2)#the real class
score = accuracy_score(small_validation["disease"], result)
print("accuracy : "+str(score))
2/2 [==============================] - 0s 8ms/step
accuracy : 0.7777777777777778
In [26]:
#writing results for exam evaluation
r= open("results_small_dataset.txt", "w+")
r.write(str(result))
r.close()